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FedLab: A Flexible Federated Learning Framework

2021-07-24Code Available1· sign in to hype

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 FedLab, a lightweight open-source framework for FL simulation. The design of FedLab focuses on FL algorithm effectiveness and communication efficiency. Also, FedLab is scalable in different deployment scenario. We hope 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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