FedProc: Prototypical contrastive federated learning on non-IID data

Xutong Mu, Yulong Shen, Ke Cheng, Xueli Geng, Jiaxuan Fu, Tao Zhang, Zhiwei Zhang

Abstract: Highlights•This paper proposes a novel federated learning framework (FedProc) to solve the non-IID data problem. The framework innovatively introduces a global class prototype to correct for local training, making the direction of local optimization consistent with the global optimization goal.•In this paper, we elaborate a generic hybrid local network architecture such that the local network takes full advantage of the potential knowledge provided by the global class prototype. The superior performance of this architecture is due to the design of a hybrid loss function.•We theoretically analyze the convergence of FedProc and obtain an upper bound on the convergence, which provides convergence guarantees for this work. In addition, experimental results show that FedProc is significantly better than the state-of-the-art methods in terms of accuracy and computational efficiency.

External IDs:dblp:journals/fgcs/MuSCGFZZ23