Trustworthy Federated Learning With Authenticated ZKPs in Mobile Edge Intelligence

Abstract

Privacy disclosure from model parameters and malicious attacks are critical issues in federated learning (FL). Existing research has yet to effectively address the simultaneous need for efficient communication design, privacy protection, and attack detection, which impedes the widespread adoption of FL in mobile edge networks over 6G wireless communication. In this paper, we propose a trustworthy FL framework that can ensure privacy, robustness, accountability, fairness, and explainability in mobile edge networks. Specifically, we integrate authenticated zero-knowledge proofs (ZKPs) and Pedersen commitments into the FL process. Despite the lack of direct access between servers and mobile devices, the servers can still identify trustworthy clients for specific tasks. Clients can verify the authenticity of the received global model based on the provided proofs and commitments. Furthermore, we leverage Ethereum to act as the verifier and authenticator of models. This verification and authentication process enables the servers to detect abnormal local models and perform trust-based aggregations. Numerical results demonstrate that the proposed trustworthy FL framework significantly improves the global model's in terms of accuracy, convergence rate, and security.