  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.
