DA-FL: Blockchain Empowered Secure and Private Federated Learning With Anonymous Authentication
Hu Xiong, Yaxin Zhao, Yutong Xia, Min Zhang, Kuo‐Hui Yeh · IEEE Transactions on Reliability · 2025
Federated learning (FL) is a secure multiparty machine learning that addresses the issue of data silos by allowing nodes to train locally. Nonetheless, the lack of trusted environments, node supervision, and privacy protection measures in centralized FL limit its large-scale promotion. To address these issues, a blockchain-based decentralized FL framework is proposed, namely, decentralized federated learning with node anonymous authentication (DA-FL). Specifically, DA-FL introduces blockchain for local model storage and global model aggregation in the absence of centralized server, and uses differential privacy to reduce the risk of model privacy leakage. In addition, a consensus mechanism proof of accuracy is designed to effectively reduce the computational load of consensus and mitigate the impact of low-quality models on the aggregation results. To achieve node supervision, distributed key generation and revocable ring signature technologies are being integrated. This ensures the anonymous authentication of nodes while also allowing for the revocation of the anonymity of malicious nodes when necessary. Finally, the security and functionality of DA-FL are evaluated through simulation experiments conducted on real datasets. The numerical results show that the proposed FL scheme has significant performance advantages over other schemes.