FedLab: A Flexible Federated Learning Framework
This work provides a flexible tool for researchers in the federated learning community to ease the implementation of novel approaches, but it is incremental as it builds on existing FL concepts without introducing new algorithms.
The authors tackled the challenge of implementing federated learning algorithms by introducing FedLab, a lightweight open-source framework for FL simulation, which focuses on algorithm effectiveness and communication efficiency, and is scalable across different deployment scenarios.
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.