DeFTA: A plug-and-play peer-to-peer decentralized federated learning framework

Yuhao Zhou, Minjia Shi, Yuxin Tian, Qing Ye, Jiancheng Lv

Abstract: Highlights•A novel model aggregating formula is proposed that eliminates the aggregating bias in decentralized FL.•Selfish workers are introduced to the decentralized FL setting, which is simple and robust against backdoor attacks.•A general decentralized FL framework is proposed that features a drop-in replacement for the centralized FedAvg algorithm.•This replacement leads to improvements in data security and system reliability, with negligible model performance loss.