FedLab: A Flexible Federated Learning Framework

Dun Zeng; Siqi Liang; Xiangjing Hu; Hui Wang; Zenglin Xu

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arXiv · 2107.11621

FedLab: A Flexible Federated Learning Framework

Dun Zeng·Siqi Liang·Xiangjing Hu·Hui Wang·Zenglin Xu

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Abstract

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.

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Dun Zeng, Siqi Liang, Xiangjing Hu, Hui Wang, Zenglin Xu. 2021-07-24. FedLab: A Flexible Federated Learning Framework. https://arxiv.org/abs/2107.11621

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