FedLG: Lightweight Generic Certificateless Authentication for Trustworthy Federated Learning in VANETs
Lang Pu, Jingjing Gu, Chao Lin, Xinyi Huang · IEEE Transactions on Information Forensics and Security · 2025
Federated learning (FL) in Vehicular Ad Hoc Networks (VANETs) enables vehicles to collaboratively train a global model for intelligent transportation systems while preserving the privacy of their local data. However, the openness and dynamic nature of VANETs introduce significant security challenges, including identity privacy leakage, model inversion attacks, and compromised model integrity. Existing cryptographic solutions, such as differential privacy and homomorphic encryption, provide partial mitigation but suffer from drawbacks including inefficiency, limited data utility, and vulnerability to data poisoning attacks. To tackle these challenges, this paper introduces FedLG, a generic certificateless (CL) authentication scheme with conditional anonymity. FedLG ensures trustworthy FL by integrating multiple security mechanisms that together guarantee model authenticity, data integrity, and privacy. Specifically, FedLG leverages Type-T signatures as a blackbox to ensure the authenticity and integrity of model parameters shared by anonymous vehicles. Additionally, we introduce a novel public key reconstruction mechanism to enhance the security of traditional CL-based systems, effectively mitigating common public key replacement attacks. FedLG also incorporates batch verification with an adaptive group batch verification algorithm, dynamically adjusting batch sizes to identify invalid signatures while preserving valid data, thereby facilitating faster model convergence. Moreover, FedLG maintains the utility of user-contributed data and can seamlessly integrate it with data poisoning attack prevention mechanisms to enhance security further. Experimental results show that FedLG is model-independent, as its integration does not affect the original model’s performance on its dataset. Moreover, it reduces the computational overhead of signature generation and verification by at least 30.8% and 56.3%, respectively, achieving an overall efficiency improvement of 49.69% compared to state-of-the-art FL authentication protocols for VANETs.