add FedBN Implementation on NVFlare research folder - a local batch normalization federated learning method

research/fedbn/LICENSE

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research/fedbn/README.md

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+# FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
+
+Welcome to the repository for FedBN, a federated learning algorithm designed to address the feature shift problem when aggregating models across different data distributions.
+
+###### Abstract:
+
+> In this work, we propose an effective method that uses local batch normalization to alleviate the feature shift before averaging models. The resulting scheme, called FedBN, outperforms both classical FedAvg and FedProx on our extensive experiments. These empirical results are supported by a convergence analysis that shows in a simplified setting that FedBN has a faster convergence rate than FedAvg.
+
+## License
+This project is open-sourced under the Apache v2 License. The codebase builds upon the initial work shared at [FedBN](https://github.com/med-air/FedBN).
+
+## Setup Instructions
+
+To set up the environment for training, execute the following commands:
+```
+pip install --upgrade pip
+pip install -r ./requirements.txt
+```
+
+## Running the code 
+
+### Initial Configuration
+
+Ensure all shell scripts are executable:
+```
+find . -name ".sh" -exec chmod +x {} \;
+``` 
+
+Set the Python path to recognize the FedBN modules:
+```
+export PYTHONPATH=${PYTHONPATH}:${PWD}/..
+```
+
+Data Preparation
+
+Download the necessary datasets by running:
+```
+./prepare_data.sh
+```
+
+# Run FedBN on different data splits
+
+FedBN (8 clients). Here we run for 50 rounds, with 4 local epochs. 
+```
+cd ./cifar10_sim_fedbn
+./run_cifar10_sim_fedbn.sh
+```
+
+Execution
+
+Run the FedBN simulation with the following command:
+```
+./run_simulator.sh cifar10_fedbn 1.0 8 8
+```
+
+> **_NOTE:_** The RESULT_ROOT=/tmp/nvflare/sim_cifar10 is set in run_simulator.sh
+
+
+## Visualizing Results
+To visualize training losses, use the plot_tensorboard_event.py script located in:
+[./custom/figs/plot_tensorboard_events.py](./custom/figs/plot_tensorboard_events.py).
+
+> **_NOTE:_** Ensure you have the necessary plotting libraries by installing them from: [./custom/figs/plot-requirements.txt](./custom/figs/plot-requirements.txt) to plot.
+
+Below is an example of the loss visualization output:
+![FedBN Loss Results](./custom/figs/savefig_example.png)
+
+
+## Citation
+If you find the code and dataset useful, please cite our paper.
+```latex
+@inproceedings{
+li2021fedbn,
+title={Fed{\{}BN{\}}: Federated Learning on Non-{\{}IID{\}} Features via Local Batch Normalization},
+author={Xiaoxiao Li and Meirui Jiang and Xiaofei Zhang and Michael Kamp and Qi Dou},
+booktitle={International Conference on Learning Representations},
+year={2021},
+url={https://openreview.net/pdf?id=6YEQUn0QICG}
+}
+```
+

research/fedbn/cifar10_sim_fedbn/jobs/cifar10_fedbn/cifar10_fedbn/config/config_fed_client.json

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+{
+    "format_version": 2,
+    "TRAIN_SPLIT_ROOT": "/tmp/cifar10_splits/cifar10_fedbn_alpha1.0_bd52371e-8aaf-4850-a5c9-33cef2d6131c",
+    "AGGREGATION_EPOCHS": 4,
+    "executors": [
+        {
+            "tasks": [
+                "train",
+                "submit_model",
+                "validate"
+            ],
+            "executor": {
+                "id": "Executor",
+                "path": "nvflare.app_common.executors.model_learner_executor.ModelLearnerExecutor",
+                "args": {
+                    "learner_id": "cifar10-learner"
+                }
+            }
+        }
+    ],
+    "task_result_filters": [],
+    "task_data_filters": [],
+    "components": [
+        {
+            "id": "cifar10-learner",
+            "path": "custom.pt.learners.CIFAR10ModelLearner",
+            "args": {
+                "train_idx_root": "{TRAIN_SPLIT_ROOT}",
+                "aggregation_epochs": "{AGGREGATION_EPOCHS}",
+                "lr": 0.01
+            }
+        }
+    ]
+}

research/fedbn/cifar10_sim_fedbn/jobs/cifar10_fedbn/cifar10_fedbn/config/config_fed_server.json

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+{
+    "format_version": 2,
+    "min_clients": 8,
+    "num_rounds": 50,
+    "TRAIN_SPLIT_ROOT": "/tmp/cifar10_splits/cifar10_fedbn_alpha1.0_bd52371e-8aaf-4850-a5c9-33cef2d6131c",
+    "alpha": 1.0,
+    "server": {
+        "heart_beat_timeout": 600
+    },
+    "task_data_filters": [],
+    "task_result_filters": [],
+    "components": [
+        {
+            "id": "data_splitter",
+            "path": "pt.utils.cifar10_data_splitter.Cifar10DataSplitter",
+            "args": {
+                "split_dir": "{TRAIN_SPLIT_ROOT}",
+                "num_sites": "{min_clients}",
+                "alpha": "{alpha}"
+            }
+        },
+        {
+            "id": "persistor",
+            "name": "PTFileModelPersistor",
+            "args": {
+                "model": {
+                    "path": "custom.pt.networks.cifar10_nets.ModerateBNCNN",
+                    "args": {}
+                }
+            }
+        },
+        {
+            "id": "model_selector",
+            "name": "IntimeModelSelector",
+            "args": {}
+        },
+        {
+            "id": "model_locator",
+            "name": "PTFileModelLocator",
+            "args": {
+                "pt_persistor_id": "persistor"
+            }
+        },
+        {
+            "id": "json_generator",
+            "name": "ValidationJsonGenerator",
+            "args": {}
+        }
+    ],
+    "workflows": [
+        {
+            "id": "fedbn_ctl",
+            "path": "custom.fedbn.FedBN",
+            "args": {
+                "min_clients": "{min_clients}",
+                "num_rounds": "{num_rounds}",
+                "persistor_id": "persistor"
+            }
+        },
+        {
+            "id": "cross_site_model_eval",
+            "name": "CrossSiteModelEval",
+            "args": {
+                "model_locator_id": "model_locator",
+                "submit_model_timeout": 600,
+                "validation_timeout": 6000,
+                "cleanup_models": true
+            }
+        }
+    ]
+}