FedAuth: A Lightweight and Privacy-Enhanced Authentication Scheme for Federated Learning in IoVs

In the Internet of Vehicles (IoVs), certificateless-based authentication has emerged as a promising approach to secure federated learning (FL) participation. However, existing certificateless-based authentication schemes still exhibit three notable limitations: (i) some schemes cannot provide security against public-key replacement (PKR) attacks and key-leakage threats; (ii) most existing schemes do not take realistic coalition attacks into account; (iii) while some schemes applicable to FL settings can offer relatively comprehensive security guarantees, they often rely on costly cryptographic operations. These limitations make it challenging for certificateless authentication frameworks to simultaneously ensure high security and low latency, particularly in resource-constrained FL deployments within IoV environments. To address these challenges, we propose FedAuth, a certificateless, lightweight, and privacy-enhanced authentication scheme tailored for FL in IoVs. First, FedAuth establishes a structured pseudonymization mechanism that enables vehicles to anonymously participate in the FL process while maintaining accountability. Afterwards, FedAuth ensures robust authentication security by cryptographically anchoring key generation to each vehicle’s pseudonym, enforcing strict key validation, and introducing hash-bound multi-layered signature verification that prevents signature recombination, thereby resisting PKR attacks, coalition attacks, and key-leakage threats. Second, FedAuth guarantees the authenticity and integrity of gradients through lightweight signature construction, which supports batch authentication while avoiding expensive operations such as bilinear pairings, thereby reducing latency. Security analysis shows that FedAuth preserves identity privacy and ensures model integrity in the face of adversarial conditions. Experimental results confirm that FedAuth significantly reduces computational overhead by at least 43.42% and saves not less than 19.8 KB of communication cost per 100 vehicles, outperforming state-of-the-art schemes in both efficiency and robustness.