BFL-SA: Blockchain-based federated learning via enhanced secure aggregation

Abstract

Federated learning, involving a central server and multiple clients, aims to keep data local but raises privacy concerns like data exposure and participation privacy. Secure aggregation, especially with pairwise masking, preserves privacy without accuracy loss. Yet, issues persist like security against malicious models, central server fault tolerance, and trust in decryption keys. Resolving these challenges is vital for advancing secure federated learning systems. In this paper, we present BFL-SA, a blockchain-based federated learning scheme via enhanced secure aggregation, which addresses key challenges by integrating blockchain consensus, publicly verifiable secret sharing, and an overdue gradients aggregation module. These enhancements significantly boost security and fault tolerance while improving the efficiency of data utilization in the secure aggregation process. After security analysis, we have demonstrated that BFL-SA achieves secure aggregation even in malicious models. Through experimental comparative analysis, BFL-SA exhibits rapid secure aggregation speed and achieves 100% model aggregation accuracy.