FedLab-benchmarks

This repo contains standard implementations of FedLab and its provided benchmarks.

Currently, following algorithms or benchrmarks are available：

• FedAvg: Communication-Efficient Learning of Deep Networks from Decentralized Data
• FedAsync: Asynchronous Federated Optimization
• LEAF: A Benchmark for Federated Settings

TODO:

• DGC: Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
• FedProx: Federated Optimization in Heterogeneous Networks

We highly welcome you to contribute federated algorithm implemented by FedLab. If you encounter any problems, do not hesitate to submit an issue or send an email to the repo maintainer.