FedMPQ: Secure and Efficient Federated Learning with Multi-codebook Product Quantization

Xu Yang, Zhuo Tang, Boyao Hao, Xiong Xiao, Jiapeng Zhang

Abstract: Secure aggregation has recently gained popularity in federated learning to defend against inference attacks by malicious aggregators or eavesdroppers. However, existing methods usually bring additional communication overhead and possibly impede the convergence rate of the global model. The challenge becomes acute in wireless network environments where bandwidth is severely constrained. Hence, the attainment of effective communication compression while ensuring secure aggregation has been a profoundly demanding and valuable quandary.In this work, we propose a novel uplink communication compression method for federated learning, named FedMPQ. It guarantees both high security and efficiency without compromising accuracy. Specifically, we introduce multiple optional codebooks for clients to ensure almost lossless information under a high compression rate. In addition, FedMPQ achieves high security with a combination of secure aggregation paradigm and differential privacy, preventing data leakage both on server and during communication. The experiments conducted on the LEAF dataset demonstrate that FedMPQ reduces the uplink communications by 90-95% with no sacrifice on accuracy.

External IDs:dblp:conf/icmcs/YangTHXZ25