Privacy-Preserving Approach to Edge Federated Learning Based on Blockchain and Fully Homomorphic Encryption

Abstract

To address the issues of high single-point failure risk, weak privacy protection, and poor resistance to poisoning attacks in edge federated learning, an edge federated learning privacy protection scheme based on blockchain and fully homomorphic encryption is proposed. This scheme uses blockchain technology combined with the CKKS (Cheon–Kim–Kim–Song) fully homomorphic encryption scheme to encrypt computational parameters. This approach reduces the risk of privacy leakage and provides edge federated learning with features such as anti-tampering, resistance to single-point failure, and data traceability. In addition, an unsupervised mechanism for identifying model gradient parameter updates is designed. This mechanism uses the consistency of historical model gradient parameter updates from edge servers as the identification basis. It can effectively detect malicious updates from edge servers, improving the accuracy of the aggregated model. Experimental results show that the proposed method can resist poisoning attacks from 70% of malicious edge servers. It offers privacy protection, transparent model aggregation, and resistance to single-point failure. Furthermore, the method achieves high model accuracy and meets stringent security, accuracy, and traceability requirements in edge federated learning scenarios.