Privacy-Preserving Byzantine-Resilient Swarm Learning for E-healthcare

Abstract Automatic medical diagnosis service based on deep learning has been entered in e-healthcare, bringing great convenience to human life. However, due to privacy regulations, insufficient data sharing among medical centers has led to many severe challenges for automated medical diagnostic services including diagnostic accuracy. To solve the problems, swarm learning, a blockchain-based Federated learning (BCFL), has been proposed. Although avoiding single point of failure attacks and offering incentive mechanism, SL still faces privacy breaches and poisoning attacks. In the paper, we propose a new privacy-preserving byzantine-resilient swarm learning (PBSL) that is resistant to poisoning attacks while protecting data privacy. Specifically, we adopt threshold holomorphic encryption to protect data privacy and provide secure aggregation. And the cosine similarity is used to judge the malicious gradient uploaded by malicious medical centers. Through security analysis, PBSL is able to defend against a variety of known security attacks. Finally, PBSL is implemented by uniting deep learning with the blockchain-based smart contract platform.Experiments based on different datasets show that PBSL algorithm is feasible and efficient.