BPVFL: A Bidirectional Privacy-Preserving Verifiable Federated Learning Framework with Homomorphic Encryption

Federated learning, while advancing data privacy, faces risks of sensitive information leakage through parameter updates, making it susceptible to inference and data reconstruction attacks. Fraudulent behaviors by central servers or clients can undermine the integrity of model training, thereby reducing accuracy and affecting decision-making quality. This paper introduces a bidirectional, privacy-preserving verifiable federated learning framework(BPVFL) built on homomorphic encryption and a novel three-party zero-knowledge protocol. This framework guarantees the integrity of server aggregation and the credibility of the information uploaded by clients. Experimental results demonstrate that BPVFL effectively protects client privacy, prevents fraud by servers and certain clients, and efficiently handles numerous client disconnections with minimal overhead.