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Merge ckks in FLServer LR Aggregator (intel-analytics#7)
* update ckks in FLServer LR Aggregator * some changes * support ckks at scala FL Server * add * add * fix and add ut * fix and add ut * add support for customized client module and divide example to 2 parties * add comments
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44
...c/main/scala/com/intel/analytics/bigdl/ppml/fl/algorithms/VFLLogisticRegressionCkks.scala
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| /* | ||
| * Copyright 2016 The BigDL Authors. | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"); | ||
| * you may not use this file except in compliance with the License. | ||
| * You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
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| package com.intel.analytics.bigdl.ppml.fl.algorithms | ||
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| import com.intel.analytics.bigdl.Module | ||
| import com.intel.analytics.bigdl.dllib.nn.{Linear, Sequential} | ||
| import com.intel.analytics.bigdl.dllib.optim.Adam | ||
| import com.intel.analytics.bigdl.dllib.utils.Log4Error | ||
| import com.intel.analytics.bigdl.ppml.fl.NNModel | ||
| import com.intel.analytics.bigdl.ppml.fl.nn.VFLNNEstimator | ||
| import com.intel.analytics.bigdl.ppml.fl.utils.FLClientClosable | ||
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| /** | ||
| * VFL Logistic Regression | ||
| * @param featureNum | ||
| * @param learningRate | ||
| */ | ||
| class VFLLogisticRegressionCkks(featureNum: Int = -1, | ||
| learningRate: Float = 0.005f, | ||
| customModel: Module[Float] = null) extends NNModel () { | ||
| Log4Error.invalidInputError (featureNum != -1 || customModel != null, | ||
| "Either featureNum or customModel should be provided") | ||
| val clientModule = if (customModel == null) { | ||
| Linear[Float] (featureNum, 1) | ||
| } else customModel | ||
| val model = Sequential[Float]().add(Linear(featureNum, 1)) | ||
| override val estimator = new VFLNNEstimator( | ||
| "vfl_logistic_regression_ckks", model, new Adam(learningRate)) | ||
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|
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| } |
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79 changes: 79 additions & 0 deletions
79
scala/ppml/src/main/scala/com/intel/analytics/bigdl/ppml/fl/example/ckks/Client.scala
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| package com.intel.analytics.bigdl.ppml.fl.example.ckks | ||
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| import com.intel.analytics.bigdl.dllib.NNContext | ||
| import com.intel.analytics.bigdl.dllib.nn.SparseLinear | ||
| import com.intel.analytics.bigdl.dllib.utils.{Log4Error, RandomGenerator} | ||
| import com.intel.analytics.bigdl.ppml.fl.NNModel | ||
| import com.intel.analytics.bigdl.ppml.fl.algorithms.{VFLLogisticRegression, VFLLogisticRegressionCkks} | ||
| import com.intel.analytics.bigdl.ppml.fl.utils.FlContextForTest | ||
| import org.apache.spark.sql.SparkSession | ||
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| class Client(trainDataPath: String, | ||
| testDataPath: String, | ||
| clientId: Int, | ||
| appName: String) extends Thread { | ||
| override def run(): Unit = { | ||
| val testFlContext = new FlContextForTest() | ||
| testFlContext.initFLContext(clientId.toString, "localhost:8980") | ||
| val sqlContext = SparkSession.builder().getOrCreate() | ||
| val pre = new DataPreprocessing(sqlContext, trainDataPath, testDataPath, clientId) | ||
| val (trainDataset, validationDataset) = pre.loadCensusData() | ||
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| val numFeature = 3049 | ||
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| RandomGenerator.RNG.setSeed(2L) | ||
| val linear = SparseLinear[Float](numFeature, 1) | ||
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| val lr: NNModel = appName match { | ||
| case "dllib" => new VFLLogisticRegression(learningRate = 0.001f, customModel = linear) | ||
| case "ckks" => new VFLLogisticRegressionCkks(learningRate = 0.001f, customModel = linear) | ||
| case _ => throw new Error() | ||
| } | ||
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| val epochNum = 20 | ||
| var accTime: Long = 0 | ||
| (0 until epochNum).foreach { epoch => | ||
| trainDataset.shuffle() | ||
| val trainData = trainDataset.toLocal().data(false) | ||
| while (trainData.hasNext) { | ||
| val miniBatch = trainData.next() | ||
| val input = miniBatch.getInput() | ||
| val currentBs = input.toTensor[Float].size(1) | ||
| val target = miniBatch.getTarget() | ||
| val dllibStart = System.nanoTime() | ||
| lr.trainStep(input, target) | ||
| accTime += System.nanoTime() - dllibStart | ||
| } | ||
| println(s"$appName Time: " + accTime / 1e9) | ||
| } | ||
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| linear.evaluate() | ||
| val evalData = validationDataset.toLocal().data(false) | ||
| var accDllib = 0 | ||
| while (evalData.hasNext) { | ||
| val miniBatch = evalData.next() | ||
| val input = miniBatch.getInput() | ||
| val currentBs = input.toTensor[Float].size(1) | ||
| val target = miniBatch.getTarget().toTensor[Float] | ||
| val predict = lr.predictStep(input) | ||
| println(s"Predicting $appName") | ||
| (0 until currentBs).foreach { i => | ||
| val dllibPre = predict.toTensor[Float].valueAt(i) | ||
| val t = target.valueAt(i + 1, 1) | ||
| if (t == 0) { | ||
| if (dllibPre <= 0.5) { | ||
| accDllib += 1 | ||
| } | ||
| } else { | ||
| if (dllibPre > 0.5) { | ||
| accDllib += 1 | ||
| } | ||
| } | ||
| // println(t + " " + dllibPre + " " + ckksPre) | ||
| } | ||
| } | ||
| println(s"$appName predict correct: $accDllib") | ||
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| } | ||
| } |
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