FedLab: A Flexible Federated Learning Framework

TL;DR

FedLab is an open-source, scalable framework designed to facilitate federated learning research by providing flexible APIs and reliable baselines, focusing on algorithm effectiveness and communication efficiency.

Contribution

Introduces FedLab, a lightweight, flexible framework for federated learning simulation that simplifies implementation and testing of new algorithms.

Findings

Supports various FL algorithms and scenarios

Enhances communication efficiency in FL simulations

Reduces development effort for researchers

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.