This repository maintains a codebase for Federated Learning research. It supports:

• PyTorch with MPI backend for a Master-Worker computation/communication topology.
• Local training can be efficiently executed in a parallel-fashion over GPUs for randomly sampled clients.
• Different FL algorithms, e.g., FedAvg, FedProx, FedAvg with Server Momentum, and FedDF, are implemented as the baselines.

We rely on Docker for our experimental environments. Please refer to the folder environments for more details.

The current repository includes

• the methods evaluated in the paper FedDF: Ensemble Distillation for Robust Model Fusion in Federated Learning. For the detailed instructions and more examples, please refer to the file codes/FedDF-code/README.md.

If you use the code in this repository, please consider to cite the following papers:

@inproceedings{lin2020ensemble,
  title={Ensemble Distillation for Robust Model Fusion in Federated Learning},
  author={Lin, Tao and Kong, Lingjing and Stich, Sebastian U and Jaggi, Martin},
  booktitle = {Advances in Neural Information Processing Systems},
  year      = {2020}
}