Federated_Learning1.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ssvakil/Machine-Learning-in-Python-Workshop/blob/master/Federated_Learning1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P5FhP3iVyPYu",
        "outputId": "74612c04-0b84-4e54-8ec5-c9d5b299bc24"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "from keras.datasets import mnist\n",
        "(train_X,train_y) , (test_X,test_Y) = mnist.load_data()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zxZvwkZYyPZA"
      },
      "source": [
        "## The accuracy of training on  centerilized learning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "Ssc7IcgUyPZD"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "WOciu5JeyPZD"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from keras.utils import to_categorical\n",
        "def prepare_data(X,Y):\n",
        "    X = X.astype(\"float32\") / 255\n",
        "    X = np.expand_dims(X, -1)\n",
        "    print(\"X:\", X.shape)\n",
        "\n",
        "    # convert class vectors to binary class matrices\n",
        "    Y = to_categorical(Y, 10)\n",
        "    return X,Y\n",
        "from tensorflow.keras import Input,Sequential\n",
        "from tensorflow.keras import layers\n",
        "def create_model():\n",
        "    model = Sequential(\n",
        "    [\n",
        "        Input(shape=(28,28,1)),\n",
        "        layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
        "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
        "        layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
        "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
        "        layers.Flatten(),\n",
        "        layers.Dropout(0.5),\n",
        "        layers.Dense(10, activation=\"softmax\"),\n",
        "    ]\n",
        "    )\n",
        "    model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
        "    return model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m33clumQyPZE",
        "outputId": "6cba0f74-e819-4a43-c0b8-01c6b8651dbc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X: (60000, 28, 28, 1)\n",
            "1875/1875 [==============================] - 22s 4ms/step - loss: 0.2114 - accuracy: 0.9356\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0563 - accuracy: 0.9828\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.05634487420320511, 0.9828000068664551]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "model = create_model()\n",
        "model.fit(*prepare_data(train_X,train_y))\n",
        "model.evaluate(*prepare_data(test_X,test_Y))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3idqVpY4yPZF"
      },
      "source": [
        "# What if data is not gathered in  single location?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "G8IMa2u4yPZF"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "def split_data(X,Y,split):\n",
        "    parts = []\n",
        "    part_len = len(X)//split\n",
        "    for i in range(split):\n",
        "        parts.append((X[i*part_len:(i+1)*part_len],Y[i*part_len:(i+1)*part_len]))\n",
        "    return parts\n",
        "splitted_data = split_data(train_X,train_y,3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gzo5U0BTyPZH",
        "outputId": "9501e04e-bf0f-4702-a653-74805b0ea492"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20000"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "len(splitted_data[0][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "a0XSx4fQyPZH"
      },
      "outputs": [],
      "source": [
        "from matplotlib import pyplot\n",
        "def showIMG(x):\n",
        "    pyplot.imshow(x, cmap=pyplot.get_cmap('gray'))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "O4GpOoEZyPZI"
      },
      "outputs": [],
      "source": [
        "models = []\n",
        "for i in range(3):\n",
        "    models.append(create_model())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eVYkoFHuyPZI",
        "outputId": "d43817cf-225d-49b6-c765-e6ad69041973"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 4s 4ms/step - loss: 0.3947 - accuracy: 0.8796\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 4s 4ms/step - loss: 0.3934 - accuracy: 0.8777\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 4s 4ms/step - loss: 0.3936 - accuracy: 0.8775\n"
          ]
        }
      ],
      "source": [
        "for i in range(len(models)):\n",
        "    models[i].fit(*prepare_data(splitted_data[i][0],splitted_data[i][1]))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "znu2q5m-yPZJ",
        "outputId": "9975a7b7-5caa-449f-9285-9e31cf73ccb0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 0.1095 - accuracy: 0.9660\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.1136 - accuracy: 0.9666\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.1173 - accuracy: 0.9654\n"
          ]
        }
      ],
      "source": [
        "for i in range(len(models)):\n",
        "    models[i].evaluate(*prepare_data(test_X,test_Y))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0SQICwO7yPZK"
      },
      "source": [
        "## Even worse if data parts are none Independent and identically distributed"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V4scKomUyPZK",
        "outputId": "40475fa4-436d-4f4d-fc64-3807c47faefa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, 1, 2]\n",
            "[3, 4, 5]\n",
            "[6, 7, 8]\n"
          ]
        }
      ],
      "source": [
        "def split_data_nonIID(X,Y,split):\n",
        "    parts = []\n",
        "    numbers = [i for i in range(10)]\n",
        "    part_len = len(numbers)//split\n",
        "    for i in range(split):\n",
        "        current_numbers = numbers[i*part_len:(i+1)*part_len]\n",
        "        print(current_numbers)\n",
        "        parts.append((np.array([x for x,y in zip(X,Y) if y in current_numbers]),\n",
        "                      np.array([y for x,y in zip(X,Y) if y in current_numbers])))\n",
        "    return parts\n",
        "splitted_data = split_data_nonIID(train_X,train_y,3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "aJzS5Be1yPZL"
      },
      "outputs": [],
      "source": [
        "models = []\n",
        "for i in range(3):\n",
        "    models.append(create_model())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1XqUNQN3yPZL",
        "outputId": "60be160d-b9dd-46bc-cec7-766ed285c780"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X: (18623, 28, 28, 1)\n",
            "582/582 [==============================] - 4s 4ms/step - loss: 0.1051 - accuracy: 0.9627\n",
            "X: (17394, 28, 28, 1)\n",
            "544/544 [==============================] - 5s 5ms/step - loss: 0.1459 - accuracy: 0.9498\n",
            "X: (18034, 28, 28, 1)\n",
            "564/564 [==============================] - 4s 4ms/step - loss: 0.1134 - accuracy: 0.9612\n"
          ]
        }
      ],
      "source": [
        "for i in range(len(models)):\n",
        "    models[i].fit(*prepare_data(splitted_data[i][0],splitted_data[i][1]))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wS_-8yo6yPZM",
        "outputId": "94f84777-dfec-4886-b9a4-dc0d8e5c05a7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 10.5052 - accuracy: 0.3133\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 11.2663 - accuracy: 0.2857\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 11.3542 - accuracy: 0.2935\n"
          ]
        }
      ],
      "source": [
        "for i in range(len(models)):\n",
        "    models[i].evaluate(*prepare_data(test_X,test_Y))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UC4r8NKKyPZM"
      },
      "source": [
        "# Federated Learning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "jBnA7khzyPZM"
      },
      "outputs": [],
      "source": [
        "class Fed_Agent:\n",
        "    def __init__(self,X,Y):\n",
        "        self.local_X = X\n",
        "        self.local_Y = Y\n",
        "        self.create_local_model()\n",
        "    def create_local_model(self):\n",
        "        self.model = create_model()\n",
        "    def local_train(self):\n",
        "        self.model.fit(*prepare_data(self.local_X,self.local_Y))\n",
        "    def evaluate(self,test_X,test_Y):\n",
        "        self.model.evaluate(*prepare_data(test_X,test_Y))\n",
        "    def get_local_weights(self):\n",
        "        return self.model.get_weights()\n",
        "    def set_local_weights(self,global_model):\n",
        "        self.model.set_weights(global_model.get_weights())\n",
        "\n",
        "class Aggregator:\n",
        "    def Aggregate(self,agents):\n",
        "        #Fed_AVG\n",
        "        new_weights = []\n",
        "        for layer in range(len(agents[0].get_local_weights())):\n",
        "            layer_weights = []\n",
        "            for agent in agents:\n",
        "                layer_weights.append(agent.get_local_weights()[layer])\n",
        "            layer_avg = np.average(layer_weights,axis=0)\n",
        "\n",
        "            new_weights.append(layer_avg)\n",
        "        global_model = create_model()\n",
        "        global_model.set_weights(new_weights)\n",
        "        return global_model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mT1vaKKHyPZN",
        "outputId": "2736bc64-d72e-4f72-d288-0e57cb60cdce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 4s 4ms/step - loss: 0.3950 - accuracy: 0.8770\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 4s 4ms/step - loss: 0.3869 - accuracy: 0.8821\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 4s 4ms/step - loss: 0.3814 - accuracy: 0.8818\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.1145 - accuracy: 0.9663\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 2s 4ms/step - loss: 0.1063 - accuracy: 0.9685\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.1072 - accuracy: 0.9695\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 1.4974 - accuracy: 0.8233\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 1.4974 - accuracy: 0.8233\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 1.4974 - accuracy: 0.8233\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.2111 - accuracy: 0.9391\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.2119 - accuracy: 0.9384\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 5ms/step - loss: 0.2110 - accuracy: 0.9391\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0941 - accuracy: 0.9704\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.1016 - accuracy: 0.9695\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0974 - accuracy: 0.9718\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0895 - accuracy: 0.9726\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0895 - accuracy: 0.9726\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0895 - accuracy: 0.9726\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.1197 - accuracy: 0.9624\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 5ms/step - loss: 0.1196 - accuracy: 0.9630\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.1192 - accuracy: 0.9646\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0665 - accuracy: 0.9792\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0745 - accuracy: 0.9753\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0684 - accuracy: 0.9800\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0616 - accuracy: 0.9803\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0616 - accuracy: 0.9803\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.0616 - accuracy: 0.9803\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0963 - accuracy: 0.9704\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0979 - accuracy: 0.9703\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.1014 - accuracy: 0.9686\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0631 - accuracy: 0.9800\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0696 - accuracy: 0.9783\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.0589 - accuracy: 0.9810\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0526 - accuracy: 0.9827\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0526 - accuracy: 0.9827\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0526 - accuracy: 0.9827\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0842 - accuracy: 0.9741\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0876 - accuracy: 0.9725\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 4s 6ms/step - loss: 0.0850 - accuracy: 0.9736\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0510 - accuracy: 0.9830\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0523 - accuracy: 0.9839\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0484 - accuracy: 0.9844\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0453 - accuracy: 0.9853\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0453 - accuracy: 0.9853\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0453 - accuracy: 0.9853\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 5ms/step - loss: 0.0699 - accuracy: 0.9775\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0741 - accuracy: 0.9765\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0746 - accuracy: 0.9771\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0431 - accuracy: 0.9851\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0467 - accuracy: 0.9847\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.0449 - accuracy: 0.9852\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0396 - accuracy: 0.9861\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0396 - accuracy: 0.9861\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0396 - accuracy: 0.9861\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0665 - accuracy: 0.9790\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 5ms/step - loss: 0.0682 - accuracy: 0.9782\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0713 - accuracy: 0.9779\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0389 - accuracy: 0.9870\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0417 - accuracy: 0.9862\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0406 - accuracy: 0.9869\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.0356 - accuracy: 0.9880\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 0.0356 - accuracy: 0.9880\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0356 - accuracy: 0.9880\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0625 - accuracy: 0.9810\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0643 - accuracy: 0.9808\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0604 - accuracy: 0.9813\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0354 - accuracy: 0.9876\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.0436 - accuracy: 0.9858\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0392 - accuracy: 0.9866\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0336 - accuracy: 0.9882\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0336 - accuracy: 0.9882\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0336 - accuracy: 0.9882\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 3ms/step - loss: 0.0565 - accuracy: 0.9815\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0602 - accuracy: 0.9806\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 5ms/step - loss: 0.0586 - accuracy: 0.9821\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0390 - accuracy: 0.9869\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0363 - accuracy: 0.9884\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0392 - accuracy: 0.9863\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0321 - accuracy: 0.9886\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0321 - accuracy: 0.9886\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0321 - accuracy: 0.9886\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 3ms/step - loss: 0.0549 - accuracy: 0.9826\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0552 - accuracy: 0.9838\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0540 - accuracy: 0.9830\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0347 - accuracy: 0.9880\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0398 - accuracy: 0.9871\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0338 - accuracy: 0.9872\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0300 - accuracy: 0.9889\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0300 - accuracy: 0.9889\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.0300 - accuracy: 0.9889\n"
          ]
        }
      ],
      "source": [
        "no = 3\n",
        "aggregator = Aggregator()\n",
        "splitted_data = split_data(train_X,train_y,no)\n",
        "agents = [Fed_Agent(*data_part) for data_part in splitted_data]\n",
        "for epoch in range(10):\n",
        "    ### TRAIN\n",
        "    print('*'*10,'TRAINING')\n",
        "    for agent in agents:\n",
        "        agent.local_train()\n",
        "    print('*'*10,'EVALUATING BEFORE AGREGATION')\n",
        "    for agent in agents:\n",
        "        agent.evaluate(test_X,test_Y)\n",
        "    global_model = aggregator.Aggregate(agents)\n",
        "    for agent in agents:\n",
        "        agent.set_local_weights(global_model)\n",
        "    print('*'*10,'EVALUATING AFTER AGREGATION')\n",
        "    for agent in agents:\n",
        "        agent.evaluate(test_X,test_Y)\n",
        "    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f6wwBnfkyPZO"
      },
      "source": [
        "# Federated learning on NonIID"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ED3_KBFyPZO",
        "outputId": "3af54fea-f6bb-4678-9524-7f04b0cc8f94"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, 1]\n",
            "[2, 3]\n",
            "[4, 5]\n",
            "[6, 7]\n",
            "[8, 9]\n",
            "********** TRAINING\n",
            "X: (12665, 28, 28, 1)\n",
            "396/396 [==============================] - 3s 5ms/step - loss: 0.0620 - accuracy: 0.9853\n",
            "X: (12089, 28, 28, 1)\n",
            "378/378 [==============================] - 3s 4ms/step - loss: 0.1445 - accuracy: 0.9451\n",
            "X: (11263, 28, 28, 1)\n",
            "352/352 [==============================] - 3s 6ms/step - loss: 0.1006 - accuracy: 0.9658\n",
            "X: (12183, 28, 28, 1)\n",
            "381/381 [==============================] - 3s 4ms/step - loss: 0.0765 - accuracy: 0.9726\n",
            "X: (11800, 28, 28, 1)\n",
            "369/369 [==============================] - 3s 4ms/step - loss: 0.1500 - accuracy: 0.9437\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 17.7657 - accuracy: 0.2112\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 11.8381 - accuracy: 0.2025\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 14.1688 - accuracy: 0.1874\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 2s 4ms/step - loss: 16.7889 - accuracy: 0.1981\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 13.4727 - accuracy: 0.1954\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 2.2187 - accuracy: 0.2884\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 2.2187 - accuracy: 0.2884\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 2.2187 - accuracy: 0.2884\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 2.2187 - accuracy: 0.2884\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 2.2187 - accuracy: 0.2884\n",
            "********** TRAINING\n",
            "X: (12665, 28, 28, 1)\n",
            "396/396 [==============================] - 2s 4ms/step - loss: 0.0307 - accuracy: 0.9943\n",
            "X: (12089, 28, 28, 1)\n",
            "378/378 [==============================] - 2s 5ms/step - loss: 0.1133 - accuracy: 0.9594\n",
            "X: (11263, 28, 28, 1)\n",
            "352/352 [==============================] - 2s 5ms/step - loss: 0.0614 - accuracy: 0.9803\n",
            "X: (12183, 28, 28, 1)\n",
            "381/381 [==============================] - 2s 4ms/step - loss: 0.0318 - accuracy: 0.9923\n",
            "X: (11800, 28, 28, 1)\n",
            "369/369 [==============================] - 2s 4ms/step - loss: 0.1323 - accuracy: 0.9552\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 34.7231 - accuracy: 0.2113\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 17.2598 - accuracy: 0.2031\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 32.3872 - accuracy: 0.1873\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 46.7077 - accuracy: 0.1984\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 24.1305 - accuracy: 0.1952\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.5702 - accuracy: 0.5016\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 1.5702 - accuracy: 0.5016\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 1.5702 - accuracy: 0.5016\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 1.5702 - accuracy: 0.5016\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 1.5702 - accuracy: 0.5016\n",
            "********** TRAINING\n",
            "X: (12665, 28, 28, 1)\n",
            "396/396 [==============================] - 2s 4ms/step - loss: 0.0131 - accuracy: 0.9964\n",
            "X: (12089, 28, 28, 1)\n",
            "378/378 [==============================] - 2s 4ms/step - loss: 0.0770 - accuracy: 0.9716\n",
            "X: (11263, 28, 28, 1)\n",
            "352/352 [==============================] - 2s 6ms/step - loss: 0.0284 - accuracy: 0.9916\n",
            "X: (12183, 28, 28, 1)\n",
            "381/381 [==============================] - 2s 4ms/step - loss: 0.0124 - accuracy: 0.9969\n",
            "X: (11800, 28, 28, 1)\n",
            "369/369 [==============================] - 2s 4ms/step - loss: 0.0889 - accuracy: 0.9719\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 29.2249 - accuracy: 0.2112\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 16.5284 - accuracy: 0.2033\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 26.4808 - accuracy: 0.1874\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 34.0953 - accuracy: 0.1984\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 23.9534 - accuracy: 0.1959\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.1075 - accuracy: 0.6949\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.1075 - accuracy: 0.6949\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 1.1075 - accuracy: 0.6949\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 1.1075 - accuracy: 0.6949\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 1.1075 - accuracy: 0.6949\n",
            "********** TRAINING\n",
            "X: (12665, 28, 28, 1)\n",
            "396/396 [==============================] - 2s 4ms/step - loss: 0.0100 - accuracy: 0.9970\n",
            "X: (12089, 28, 28, 1)\n",
            "378/378 [==============================] - 2s 5ms/step - loss: 0.0555 - accuracy: 0.9806\n",
            "X: (11263, 28, 28, 1)\n",
            "352/352 [==============================] - 2s 5ms/step - loss: 0.0202 - accuracy: 0.9941\n",
            "X: (12183, 28, 28, 1)\n",
            "381/381 [==============================] - 2s 6ms/step - loss: 0.0094 - accuracy: 0.9967\n",
            "X: (11800, 28, 28, 1)\n",
            "369/369 [==============================] - 2s 4ms/step - loss: 0.0716 - accuracy: 0.9764\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 30.8520 - accuracy: 0.2113\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 13.7192 - accuracy: 0.2022\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 24.8777 - accuracy: 0.1874\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 29.4489 - accuracy: 0.1984\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 14.0521 - accuracy: 0.1952\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 3.0292 - accuracy: 0.4247\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 3.0292 - accuracy: 0.4247\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 3.0292 - accuracy: 0.4247\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 3.0292 - accuracy: 0.4247\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 3.0292 - accuracy: 0.4247\n",
            "********** TRAINING\n",
            "X: (12665, 28, 28, 1)\n",
            "396/396 [==============================] - 2s 4ms/step - loss: 0.0073 - accuracy: 0.9976\n",
            "X: (12089, 28, 28, 1)\n",
            "378/378 [==============================] - 2s 4ms/step - loss: 0.0743 - accuracy: 0.9773\n",
            "X: (11263, 28, 28, 1)\n",
            "352/352 [==============================] - 1s 4ms/step - loss: 0.0197 - accuracy: 0.9940\n",
            "X: (12183, 28, 28, 1)\n",
            "381/381 [==============================] - 2s 6ms/step - loss: 0.0051 - accuracy: 0.9989\n",
            "X: (11800, 28, 28, 1)\n",
            "369/369 [==============================] - 2s 4ms/step - loss: 0.0957 - accuracy: 0.9750\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 12.4033 - accuracy: 0.2112\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 13.2202 - accuracy: 0.2032\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 15.9406 - accuracy: 0.1873\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 12.9929 - accuracy: 0.1984\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 16.9891 - accuracy: 0.1964\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 3.9568 - accuracy: 0.3407\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 3.9568 - accuracy: 0.3407\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 3.9568 - accuracy: 0.3407\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 3.9568 - accuracy: 0.3407\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 3.9568 - accuracy: 0.3407\n",
            "********** TRAINING\n",
            "X: (12665, 28, 28, 1)\n",
            "396/396 [==============================] - 2s 4ms/step - loss: 0.0438 - accuracy: 0.9909\n",
            "X: (12089, 28, 28, 1)\n",
            "378/378 [==============================] - 2s 4ms/step - loss: 0.0477 - accuracy: 0.9835\n",
            "X: (11263, 28, 28, 1)\n",
            "352/352 [==============================] - 2s 6ms/step - loss: 0.0346 - accuracy: 0.9906\n",
            "X: (12183, 28, 28, 1)\n",
            "381/381 [==============================] - 2s 4ms/step - loss: 0.0457 - accuracy: 0.9915\n",
            "X: (11800, 28, 28, 1)\n",
            "369/369 [==============================] - 2s 4ms/step - loss: 0.0500 - accuracy: 0.9840\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 15.0479 - accuracy: 0.2112\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 9.7890 - accuracy: 0.2034\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 16.6923 - accuracy: 0.1873\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 15.2196 - accuracy: 0.1984\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 10.7585 - accuracy: 0.1961\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 2.3813 - accuracy: 0.4959\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 2.3813 - accuracy: 0.4959\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 2.3813 - accuracy: 0.4959\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 2.3813 - accuracy: 0.4959\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 2.3813 - accuracy: 0.4959\n",
            "********** TRAINING\n",
            "X: (12665, 28, 28, 1)\n",
            "396/396 [==============================] - 2s 4ms/step - loss: 0.0071 - accuracy: 0.9982\n",
            "X: (12089, 28, 28, 1)\n",
            "378/378 [==============================] - 2s 4ms/step - loss: 0.0909 - accuracy: 0.9721\n",
            "X: (11263, 28, 28, 1)\n",
            "352/352 [==============================] - 2s 4ms/step - loss: 0.0172 - accuracy: 0.9949\n",
            "X: (12183, 28, 28, 1)\n",
            "381/381 [==============================] - 2s 5ms/step - loss: 0.0037 - accuracy: 0.9997\n",
            "X: (11800, 28, 28, 1)\n",
            "369/369 [==============================] - 2s 4ms/step - loss: 0.1269 - accuracy: 0.9638\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 12.4540 - accuracy: 0.2112\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 10.3438 - accuracy: 0.2035\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 14.6949 - accuracy: 0.1874\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 10.1575 - accuracy: 0.1984\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 12.6329 - accuracy: 0.1964\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.4366 - accuracy: 0.4797\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 1.4366 - accuracy: 0.4797\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 1.4366 - accuracy: 0.4797\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 1.4366 - accuracy: 0.4797\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 1.4366 - accuracy: 0.4797\n",
            "********** TRAINING\n",
            "X: (12665, 28, 28, 1)\n",
            "396/396 [==============================] - 2s 4ms/step - loss: 0.0151 - accuracy: 0.9951\n",
            "X: (12089, 28, 28, 1)\n",
            "378/378 [==============================] - 2s 4ms/step - loss: 0.0390 - accuracy: 0.9873\n",
            "X: (11263, 28, 28, 1)\n",
            "352/352 [==============================] - 2s 6ms/step - loss: 0.0227 - accuracy: 0.9926\n",
            "X: (12183, 28, 28, 1)\n",
            "381/381 [==============================] - 2s 5ms/step - loss: 0.0170 - accuracy: 0.9946\n",
            "X: (11800, 28, 28, 1)\n",
            "369/369 [==============================] - 2s 4ms/step - loss: 0.0482 - accuracy: 0.9845\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 15.5808 - accuracy: 0.2113\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 6.2509 - accuracy: 0.2035\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 13.6652 - accuracy: 0.1874\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 14.8535 - accuracy: 0.1985\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 7.2066 - accuracy: 0.1971\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 2.1143 - accuracy: 0.5568\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 2.1143 - accuracy: 0.5568\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 2.1143 - accuracy: 0.5568\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 2.1143 - accuracy: 0.5568\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 2.1143 - accuracy: 0.5568\n",
            "********** TRAINING\n",
            "X: (12665, 28, 28, 1)\n",
            "396/396 [==============================] - 2s 4ms/step - loss: 0.0052 - accuracy: 0.9985\n",
            "X: (12089, 28, 28, 1)\n",
            "378/378 [==============================] - 2s 4ms/step - loss: 0.0671 - accuracy: 0.9809\n",
            "X: (11263, 28, 28, 1)\n",
            "352/352 [==============================] - 2s 6ms/step - loss: 0.0102 - accuracy: 0.9963\n",
            "X: (12183, 28, 28, 1)\n",
            "381/381 [==============================] - 2s 5ms/step - loss: 0.0022 - accuracy: 0.9996\n",
            "X: (11800, 28, 28, 1)\n",
            "369/369 [==============================] - 2s 4ms/step - loss: 0.0701 - accuracy: 0.9784\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 8.1934 - accuracy: 0.2113\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 9.9615 - accuracy: 0.2039\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 13.9343 - accuracy: 0.1874\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 8.3764 - accuracy: 0.1984\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 13.3406 - accuracy: 0.1969\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 2.4926 - accuracy: 0.3810\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 2.4926 - accuracy: 0.3810\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 2.4926 - accuracy: 0.3810\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 2.4926 - accuracy: 0.3810\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 2.4926 - accuracy: 0.3810\n",
            "********** TRAINING\n",
            "X: (12665, 28, 28, 1)\n",
            "396/396 [==============================] - 2s 4ms/step - loss: 0.0230 - accuracy: 0.9933\n",
            "X: (12089, 28, 28, 1)\n",
            "378/378 [==============================] - 2s 4ms/step - loss: 0.0325 - accuracy: 0.9890\n",
            "X: (11263, 28, 28, 1)\n",
            "352/352 [==============================] - 2s 4ms/step - loss: 0.0217 - accuracy: 0.9929\n",
            "X: (12183, 28, 28, 1)\n",
            "381/381 [==============================] - 2s 6ms/step - loss: 0.0280 - accuracy: 0.9927\n",
            "X: (11800, 28, 28, 1)\n",
            "369/369 [==============================] - 2s 4ms/step - loss: 0.0390 - accuracy: 0.9881\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 15.5260 - accuracy: 0.2114\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 6.8419 - accuracy: 0.2035\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 16.5268 - accuracy: 0.1874\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 12.2776 - accuracy: 0.1984\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 7.6679 - accuracy: 0.1971\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 2.3047 - accuracy: 0.5580\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 2.3047 - accuracy: 0.5580\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 2.3047 - accuracy: 0.5580\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 2.3047 - accuracy: 0.5580\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 2.3047 - accuracy: 0.5580\n"
          ]
        }
      ],
      "source": [
        "no=5\n",
        "splitted_data = split_data_nonIID(train_X,train_y,no)\n",
        "aggregator = Aggregator()\n",
        "agents = [Fed_Agent(*data_part) for data_part in splitted_data]\n",
        "for epoch in range(10):\n",
        "    ### TRAIN\n",
        "    print('*'*10,'TRAINING')\n",
        "    for agent in agents:\n",
        "        agent.local_train()\n",
        "    print('*'*10,'EVALUATING BEFORE AGREGATION')\n",
        "    for agent in agents:\n",
        "        agent.evaluate(test_X,test_Y)\n",
        "    global_model = aggregator.Aggregate(agents)\n",
        "    for agent in agents:\n",
        "        agent.set_local_weights(global_model)\n",
        "    print('*'*10,'EVALUATING AFTER AGREGATION')\n",
        "    for agent in agents:\n",
        "        agent.evaluate(test_X,test_Y)\n",
        "    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-8F4XAE4yPZP"
      },
      "source": [
        "# Attacking federated learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eB7s5r9QyPZP"
      },
      "source": [
        "## Data Poisoing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "eZi6VPdWyPZQ"
      },
      "outputs": [],
      "source": [
        "class Attacker(Fed_Agent):\n",
        "    def __init__(self,X,Y):\n",
        "        self.local_X = X\n",
        "        self.local_Y = np.array([(y+1)%10 for y in Y])\n",
        "        self.create_local_model()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NsO_LI6-yPZQ",
        "outputId": "8db1abad-6fd2-4c72-bf06-cefe536c6dc1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 4s 4ms/step - loss: 0.3994 - accuracy: 0.8795\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 5s 5ms/step - loss: 0.3915 - accuracy: 0.8788\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 4s 4ms/step - loss: 0.3859 - accuracy: 0.8824\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.1018 - accuracy: 0.9707\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.1012 - accuracy: 0.9701\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 11.3424 - accuracy: 0.0017\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 1.7483 - accuracy: 0.6351\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 1.7483 - accuracy: 0.6351\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 1.7483 - accuracy: 0.6351\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.2334 - accuracy: 0.9327\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.2239 - accuracy: 0.9311\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.2652 - accuracy: 0.9182\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.0946 - accuracy: 0.9735\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0897 - accuracy: 0.9721\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 11.5683 - accuracy: 0.0015\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3274 - accuracy: 0.9196\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3274 - accuracy: 0.9196\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3274 - accuracy: 0.9196\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.1486 - accuracy: 0.9539\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.1480 - accuracy: 0.9546\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.2325 - accuracy: 0.9293\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0746 - accuracy: 0.9777\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0714 - accuracy: 0.9785\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 11.6542 - accuracy: 0.0025\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.5086 - accuracy: 0.8373\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.5086 - accuracy: 0.8373\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 0.5086 - accuracy: 0.8373\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.1256 - accuracy: 0.9620\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.1189 - accuracy: 0.9635\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.2007 - accuracy: 0.9388\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0644 - accuracy: 0.9816\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0671 - accuracy: 0.9795\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 12.1677 - accuracy: 0.0020\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 0.4076 - accuracy: 0.8724\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.4076 - accuracy: 0.8724\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.4076 - accuracy: 0.8724\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.1026 - accuracy: 0.9686\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.1081 - accuracy: 0.9654\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.1812 - accuracy: 0.9433\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.0654 - accuracy: 0.9789\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.0559 - accuracy: 0.9826\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 12.0797 - accuracy: 0.0025\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.4715 - accuracy: 0.8372\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.4715 - accuracy: 0.8372\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.4715 - accuracy: 0.8372\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0980 - accuracy: 0.9702\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0942 - accuracy: 0.9701\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.1684 - accuracy: 0.9467\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0509 - accuracy: 0.9830\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0490 - accuracy: 0.9847\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 12.7819 - accuracy: 0.0025\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3766 - accuracy: 0.8811\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3766 - accuracy: 0.8811\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3766 - accuracy: 0.8811\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0918 - accuracy: 0.9718\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0889 - accuracy: 0.9717\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 3ms/step - loss: 0.1585 - accuracy: 0.9497\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0508 - accuracy: 0.9835\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0445 - accuracy: 0.9856\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 12.9214 - accuracy: 0.0036\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3708 - accuracy: 0.8794\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3708 - accuracy: 0.8794\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.3708 - accuracy: 0.8794\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0822 - accuracy: 0.9758\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 3ms/step - loss: 0.0857 - accuracy: 0.9728\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 3ms/step - loss: 0.1464 - accuracy: 0.9543\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0564 - accuracy: 0.9816\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0447 - accuracy: 0.9858\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 12.5335 - accuracy: 0.0028\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3364 - accuracy: 0.9019\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.3364 - accuracy: 0.9019\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3364 - accuracy: 0.9019\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0765 - accuracy: 0.9766\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0801 - accuracy: 0.9754\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.1474 - accuracy: 0.9525\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0507 - accuracy: 0.9822\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 0.0396 - accuracy: 0.9868\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 12.2723 - accuracy: 0.0023\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3857 - accuracy: 0.8559\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3857 - accuracy: 0.8559\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3857 - accuracy: 0.8559\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0737 - accuracy: 0.9765\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 5ms/step - loss: 0.0744 - accuracy: 0.9768\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.1464 - accuracy: 0.9536\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0422 - accuracy: 0.9860\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0411 - accuracy: 0.9861\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 12.7579 - accuracy: 0.0018\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3267 - accuracy: 0.9039\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3267 - accuracy: 0.9039\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 0.3267 - accuracy: 0.9039\n"
          ]
        }
      ],
      "source": [
        "no=3\n",
        "splitted_data = split_data(train_X,train_y,no)\n",
        "aggregator = Aggregator()\n",
        "agents = [Fed_Agent(*data_part) for data_part in splitted_data]\n",
        "agents[-1] = Attacker(*splitted_data[-1])\n",
        "for epoch in range(10):\n",
        "    ### TRAIN\n",
        "    print('*'*10,'TRAINING')\n",
        "    for agent in agents:\n",
        "        agent.local_train()\n",
        "    print('*'*10,'EVALUATING BEFORE AGREGATION')\n",
        "    for agent in agents:\n",
        "        agent.evaluate(test_X,test_Y)\n",
        "    global_model = aggregator.Aggregate(agents)\n",
        "    for agent in agents:\n",
        "        agent.set_local_weights(global_model)\n",
        "    print('*'*10,'EVALUATING AFTER AGREGATION')\n",
        "    for agent in agents:\n",
        "        agent.evaluate(test_X,test_Y)\n",
        "    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WwSqPWgyyPZR"
      },
      "source": [
        "## Model Poisoning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "h27BFzqFyPZR"
      },
      "outputs": [],
      "source": [
        "\n",
        "class Attacker(Fed_Agent):\n",
        "    def local_train(self):\n",
        "        print('HAHA. I do nothing for traning')\n",
        "    def get_local_weights(self):\n",
        "        weights = super().get_local_weights()\n",
        "        new_weights = [np.random.rand(*w.shape) for w in weights]\n",
        "        return new_weights\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FkBaEKCfyPZS",
        "outputId": "f37f66bf-1f7d-4825-ec06-c2429a1dd665"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 4s 4ms/step - loss: 0.4000 - accuracy: 0.8744\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 4s 4ms/step - loss: 0.3837 - accuracy: 0.8787\n",
            "HAHA. I do nothing for traning\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.1105 - accuracy: 0.9673\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 2s 5ms/step - loss: 0.1054 - accuracy: 0.9680\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 2.3225 - accuracy: 0.1070\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 215.9604 - accuracy: 0.2118\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 215.9604 - accuracy: 0.2118\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 215.9604 - accuracy: 0.2118\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 10.4943 - accuracy: 0.5586\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 11.7434 - accuracy: 0.5512\n",
            "HAHA. I do nothing for traning\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.6371 - accuracy: 0.8490\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.6414 - accuracy: 0.8519\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 215.9604 - accuracy: 0.2118\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 159.6766 - accuracy: 0.2169\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 159.6766 - accuracy: 0.2169\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 159.6766 - accuracy: 0.2169\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 9.2873 - accuracy: 0.5271\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 9.9911 - accuracy: 0.5035\n",
            "HAHA. I do nothing for traning\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.8039 - accuracy: 0.8083\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.8779 - accuracy: 0.8024\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 159.6766 - accuracy: 0.2169\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 236.0116 - accuracy: 0.2857\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 236.0116 - accuracy: 0.2857\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 236.0116 - accuracy: 0.2857\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 5ms/step - loss: 14.0042 - accuracy: 0.4915\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 14.0819 - accuracy: 0.4972\n",
            "HAHA. I do nothing for traning\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.8542 - accuracy: 0.7746\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.8383 - accuracy: 0.7776\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 236.0116 - accuracy: 0.2857\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 128.3744 - accuracy: 0.4758\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 128.3744 - accuracy: 0.4758\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 128.3744 - accuracy: 0.4758\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 14.7825 - accuracy: 0.5101\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 14.9830 - accuracy: 0.5043\n",
            "HAHA. I do nothing for traning\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.8691 - accuracy: 0.7782\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.8300 - accuracy: 0.7856\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 128.3744 - accuracy: 0.4758\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 328.1994 - accuracy: 0.2928\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 328.1994 - accuracy: 0.2928\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 328.1994 - accuracy: 0.2928\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 16.7171 - accuracy: 0.4944\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 15.9532 - accuracy: 0.4820\n",
            "HAHA. I do nothing for traning\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.8371 - accuracy: 0.7707\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.8708 - accuracy: 0.7628\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 328.1994 - accuracy: 0.2928\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 472.7155 - accuracy: 0.2066\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 472.7155 - accuracy: 0.2066\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 472.7155 - accuracy: 0.2066\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 17.3944 - accuracy: 0.4941\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 18.5982 - accuracy: 0.4775\n",
            "HAHA. I do nothing for traning\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.8782 - accuracy: 0.7551\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 5ms/step - loss: 0.8854 - accuracy: 0.7636\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 472.7155 - accuracy: 0.2066\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 160.5531 - accuracy: 0.4963\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 160.5531 - accuracy: 0.4963\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 160.5531 - accuracy: 0.4963\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 16.2789 - accuracy: 0.5023\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 17.5048 - accuracy: 0.5050\n",
            "HAHA. I do nothing for traning\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.8607 - accuracy: 0.7828\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.8319 - accuracy: 0.7744\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 160.5531 - accuracy: 0.4963\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 293.9354 - accuracy: 0.2993\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 293.9354 - accuracy: 0.2993\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 293.9354 - accuracy: 0.2993\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 17.3298 - accuracy: 0.5027\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 18.0219 - accuracy: 0.4877\n",
            "HAHA. I do nothing for traning\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.8753 - accuracy: 0.7598\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.8633 - accuracy: 0.7642\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 293.9354 - accuracy: 0.2993\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 60.3548 - accuracy: 0.6923\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 60.3548 - accuracy: 0.6923\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 60.3548 - accuracy: 0.6923\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 5ms/step - loss: 19.3943 - accuracy: 0.4955\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 5ms/step - loss: 19.6972 - accuracy: 0.5089\n",
            "HAHA. I do nothing for traning\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.8357 - accuracy: 0.7957\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.7911 - accuracy: 0.7965\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 60.3548 - accuracy: 0.6923\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 186.4794 - accuracy: 0.4993\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 186.4794 - accuracy: 0.4993\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 186.4794 - accuracy: 0.4993\n"
          ]
        }
      ],
      "source": [
        "no=3\n",
        "splitted_data = split_data(train_X,train_y,no)\n",
        "aggregator = Aggregator()\n",
        "agents = [Fed_Agent(*data_part) for data_part in splitted_data]\n",
        "agents[-1] = Attacker(*splitted_data[-1])\n",
        "for epoch in range(10):\n",
        "    ### TRAIN\n",
        "    print('*'*10,'TRAINING')\n",
        "    for agent in agents:\n",
        "        agent.local_train()\n",
        "    print('*'*10,'EVALUATING BEFORE AGREGATION')\n",
        "    for agent in agents:\n",
        "        agent.evaluate(test_X,test_Y)\n",
        "    global_model = aggregator.Aggregate(agents)\n",
        "    for agent in agents:\n",
        "        agent.set_local_weights(global_model)\n",
        "    print('*'*10,'EVALUATING AFTER AGREGATION')\n",
        "    for agent in agents:\n",
        "        agent.evaluate(test_X,test_Y)\n",
        "    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EPzrNYD_yPZS"
      },
      "source": [
        "## selfish attacker "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "hIHO1sTAyPZS"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "class Attacker(Fed_Agent):\n",
        "    def __init__(self):\n",
        "        self.create_local_model()\n",
        "    def create_local_model(self):\n",
        "        self.model = create_model()\n",
        "    def local_train(self):\n",
        "        print('.....IM TRAINING. wait.........')\n",
        "    def evaluate(self,test_X,test_Y):\n",
        "        self.model.evaluate(*prepare_data(test_X,test_Y))\n",
        "    def get_local_weights(self):\n",
        "        return self.model.get_weights()\n",
        "    def set_local_weights(self,global_model):\n",
        "        self.model.set_weights(global_model.get_weights())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P9L_0QQkyPZT",
        "outputId": "8c77d28a-1222-4586-a207-06db9ecc9fb2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 4s 4ms/step - loss: 0.3956 - accuracy: 0.8748\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 4s 4ms/step - loss: 0.3898 - accuracy: 0.8800\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 4s 4ms/step - loss: 0.3750 - accuracy: 0.8843\n",
            ".....IM TRAINING. wait.........\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.1036 - accuracy: 0.9684\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.1132 - accuracy: 0.9649\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.1101 - accuracy: 0.9663\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 2.3270 - accuracy: 0.0844\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 2.0162 - accuracy: 0.8303\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 2.0162 - accuracy: 0.8303\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 2.0162 - accuracy: 0.8303\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 2.0162 - accuracy: 0.8303\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.2678 - accuracy: 0.9233\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.2627 - accuracy: 0.9239\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.2589 - accuracy: 0.9270\n",
            ".....IM TRAINING. wait.........\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.1179 - accuracy: 0.9644\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.1072 - accuracy: 0.9682\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.1098 - accuracy: 0.9662\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 2.0162 - accuracy: 0.8303\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.1813 - accuracy: 0.9682\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.1813 - accuracy: 0.9682\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.1813 - accuracy: 0.9682\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.1813 - accuracy: 0.9682\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.1429 - accuracy: 0.9578\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 5ms/step - loss: 0.1472 - accuracy: 0.9538\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.1468 - accuracy: 0.9564\n",
            ".....IM TRAINING. wait.........\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0787 - accuracy: 0.9747\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0833 - accuracy: 0.9734\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0822 - accuracy: 0.9741\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.1813 - accuracy: 0.9682\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.0857 - accuracy: 0.9743\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0857 - accuracy: 0.9743\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0857 - accuracy: 0.9743\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0857 - accuracy: 0.9743\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.1148 - accuracy: 0.9648\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.1157 - accuracy: 0.9650\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 5ms/step - loss: 0.1148 - accuracy: 0.9656\n",
            ".....IM TRAINING. wait.........\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0767 - accuracy: 0.9761\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0672 - accuracy: 0.9784\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0717 - accuracy: 0.9761\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0857 - accuracy: 0.9743\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0670 - accuracy: 0.9783\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0670 - accuracy: 0.9783\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.0670 - accuracy: 0.9783\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0670 - accuracy: 0.9783\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0991 - accuracy: 0.9685\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0981 - accuracy: 0.9686\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0987 - accuracy: 0.9703\n",
            ".....IM TRAINING. wait.........\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0596 - accuracy: 0.9811\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.0577 - accuracy: 0.9820\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0588 - accuracy: 0.9816\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0670 - accuracy: 0.9783\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0539 - accuracy: 0.9825\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0539 - accuracy: 0.9825\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0539 - accuracy: 0.9825\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0539 - accuracy: 0.9825\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0863 - accuracy: 0.9745\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 5ms/step - loss: 0.0877 - accuracy: 0.9731\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0909 - accuracy: 0.9711\n",
            ".....IM TRAINING. wait.........\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0565 - accuracy: 0.9811\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0602 - accuracy: 0.9811\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0592 - accuracy: 0.9802\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0539 - accuracy: 0.9825\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.0483 - accuracy: 0.9834\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0483 - accuracy: 0.9834\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0483 - accuracy: 0.9834\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0483 - accuracy: 0.9834\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0806 - accuracy: 0.9758\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0823 - accuracy: 0.9742\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0807 - accuracy: 0.9753\n",
            ".....IM TRAINING. wait.........\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.0575 - accuracy: 0.9818\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0539 - accuracy: 0.9823\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0546 - accuracy: 0.9823\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0483 - accuracy: 0.9834\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0452 - accuracy: 0.9848\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0452 - accuracy: 0.9848\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0452 - accuracy: 0.9848\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0452 - accuracy: 0.9848\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0718 - accuracy: 0.9787\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0756 - accuracy: 0.9766\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0712 - accuracy: 0.9776\n",
            ".....IM TRAINING. wait.........\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0522 - accuracy: 0.9834\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0474 - accuracy: 0.9849\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0436 - accuracy: 0.9852\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0452 - accuracy: 0.9848\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0418 - accuracy: 0.9859\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.0418 - accuracy: 0.9859\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.0418 - accuracy: 0.9859\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0418 - accuracy: 0.9859\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0663 - accuracy: 0.9789\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0683 - accuracy: 0.9789\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0714 - accuracy: 0.9780\n",
            ".....IM TRAINING. wait.........\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0466 - accuracy: 0.9847\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 4ms/step - loss: 0.0444 - accuracy: 0.9865\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0445 - accuracy: 0.9854\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0418 - accuracy: 0.9859\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0388 - accuracy: 0.9864\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0388 - accuracy: 0.9864\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0388 - accuracy: 0.9864\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0388 - accuracy: 0.9864\n",
            "********** TRAINING\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0641 - accuracy: 0.9796\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0666 - accuracy: 0.9780\n",
            "X: (20000, 28, 28, 1)\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0636 - accuracy: 0.9797\n",
            ".....IM TRAINING. wait.........\n",
            "********** EVALUATING BEFORE AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0411 - accuracy: 0.9870\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0423 - accuracy: 0.9855\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0404 - accuracy: 0.9859\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0388 - accuracy: 0.9864\n",
            "********** EVALUATING AFTER AGREGATION\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0358 - accuracy: 0.9876\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0358 - accuracy: 0.9876\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0358 - accuracy: 0.9876\n",
            "X: (10000, 28, 28, 1)\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0358 - accuracy: 0.9876\n"
          ]
        }
      ],
      "source": [
        "no=3\n",
        "splitted_data = split_data(train_X,train_y,no)\n",
        "aggregator = Aggregator()\n",
        "agents = [Fed_Agent(*data_part) for data_part in splitted_data]\n",
        "agents.append(Attacker())\n",
        "for epoch in range(10):\n",
        "    ### TRAIN\n",
        "    print('*'*10,'TRAINING')\n",
        "    for agent in agents:\n",
        "        agent.local_train()\n",
        "    print('*'*10,'EVALUATING BEFORE AGREGATION')\n",
        "    for agent in agents:\n",
        "        agent.evaluate(test_X,test_Y)\n",
        "    global_model = aggregator.Aggregate(agents)\n",
        "    for agent in agents:\n",
        "        agent.set_local_weights(global_model)\n",
        "    print('*'*10,'EVALUATING AFTER AGREGATION')\n",
        "    for agent in agents:\n",
        "        agent.evaluate(test_X,test_Y)\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "hpRXga80yPZT"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.5"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}