PBFL: A Privacy-Preserving Blockchain-Based Federated Learning Framework With Homomorphic Encryption and Single Masking
Baofu Han, Bing Li, Raja Jurdak, Peiyun Zhang, Hao Zhang, Pan Feng, Chau Yuen
Abstract
Federated Learning (FL) has emerged as a promising paradigm for secure data sharing in Industrial Internet of Things (IIoT), enabling collaborative model training without direct exchange of raw data. However, recent studies have shown that FL still suffers from privacy vulnerabilities, where adversaries can reconstruct sensitive information by analyzing shared model parameters. Although several privacy-preserving FL (PPFL) schemes have been proposed to address these challenges, they primarily focus on protecting local model privacy, with limited attention to protecting global model confidentiality during aggregation. Additionally, their reliance on centralized aggregation servers introduces risks of single points of failure. To address these challenges, we propose a novel privacy-preserving blockchain-based FL framework (PBFL) that integrates blockchain, homomorphic encryption (HE), and a single masking. Specifically, PBFL employs HE to enable secure model training within the ciphertext domain, ensuring global model confidentiality. The single masking technique allows clients to apply unique random masks to their encrypted local model updates, enabling secure aggregation while preserving local privacy. Additionally, PBFL leverages blockchain for decentralized aggregation and encrypted model storage, effectively mitigating the risks associated with centralized servers. Experimental results demonstrate that PBFL achieves comparable model accuracy to state-of-the-art solutions while providing enhanced privacy protection. Furthermore, even with a client dropout rate of up to 30%, PBFL outperforms other blockchain-based PPFL methods in terms of computational and communication efficiency.