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.
翻译:联邦学习(FL)是一个机械学习领域,研究人员试图在不违反隐私保护条例的情况下促进多党之间的示范学习过程,在FL优化和通信相关研究方面投入了大量精力,在这项工作中,我们引入了FedLab,这是FL模拟的轻量级开放源码框架,FedLab的设计侧重于FL算法的有效性和通信效率。此外,FedLab在不同的部署情景下是可以伸缩的。我们希望FedLab能够提供灵活的API以及可靠的基线实施,并减轻FL社区研究人员实施新办法的负担。