/pytorch_federated_learning

PyTorch Federated Learning

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PyTorch Implementation of Federated Learning Baselines

PyTorch-Federated-Learning provides various federated learning baselines implemented using the PyTorch framework. The codebase follows a client-server architecture and is highly intuitive and accessible.

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  • Current Baseline implementations: Pytorch implementations of the federated learning baselines. The currently supported baselines are FedAvg, FedNova, FedProx and SCAFFOLD:

    • FedAvg (Hugh Brendan McMahan et al., AISTATS 2017)
    • FedNova (Jianyu Wang et al., NeurIPS 2020) :octocat:
    • FedProx (Tian Li et al., MLSys 2020) :octocat:
    • SCAFFOLD (Sai Praneeth Karimireddy et al.,ICML 2020) :octocat:

  • Dataset preprocessing: Downloading the benchmark datasets automatically and dividing them into a number of clients w.r.t. federated settings. The currently supported datasets are MNIST, Fashion-MNIST, SVHN, CIFAR-10, CIFAR-100. Other datasets need to be downloaded manually.

  • Postprocessing: Visualization of the training results for evaluation.

Installation

Dependencies

  • Python (3.8)
  • PyTorch (1.8.1)
  • OpenCV (4.5)
  • numpy (1.21.5)

Install requirements

Run: pip install -r requirements.txt to install the required packages.

Federated Dataset Preprocessing

This preprocessing aims to divide the entire datasets into a dedicated number of clients with respect to federated settings. Depending on the the number of classes in each local dataset, the entire dataset are split into Non-IID datasets in terms of label distribution skew.

Execute the Federated Learning Baselines

Test Run

Hyperparameters are defined in a yaml file, e.g. "./config/test_config.yaml", and then just run with this configuration:

python fl_main.py --config "./config/test_config.yaml"

Evaluation Procedures

Please run python postprocessing/eval_main.py -rr 'results' to plot the testing accuracy and training loss by the increasing number of epochs or communication rounds. Note that the labels in the figure is the name of result files