Federated learning (FL) is a machine learning field in which researchers try to facilitate model learning process among multiparty without violating privacy protection regulations. Considerable effort has been invested in FL optimization and communication related researches. In this work, we introduce \texttt{FedLab}, a lightweight open-source framework for FL simulation. The design of \texttt{FedLab} focuses on FL algorithm effectiveness and communication efficiency. Also, \texttt{FedLab} is scalable in different deployment scenario. We hope \texttt{FedLab} could provide flexible API as well as reliable baseline implementations, and relieve the burden of implementing novel approaches for researchers in FL community.
@article{arxiv.2107.11621,
title = {FedLab: A Flexible Federated Learning Framework},
author = {Dun Zeng and Siqi Liang and Xiangjing Hu and Hui Wang and Zenglin Xu},
journal= {arXiv preprint arXiv:2107.11621},
year = {2022}
}