A lightweight anonymous authentication scheme for federated learning

Federated learning enables collaborative model training between central servers and distributed clients without collecting users' raw sensitive data, which effectively promotes the large-scale deployment of intelligent collaborative services. Considering the high sensitivity of local training data and model gradient parameters in federated learning, protecting identity privacy and interaction security has become extremely critical. Therefore, mutual identity authentication is indispensable to restrict illegal client access and prevent malicious parameter transmission and data tampering. In this paper, we propose a lightweight anonymous authentication scheme for federated learning (FedLAS), which realizes secure mutual authentication between servers and clients and establishes a shared session key for subsequent encrypted interaction. In particular, the proposed scheme eliminates the reliance on high-cost cryptographic operations such as bilinear pairing, thus minimizing computational and communication overhead. Furthermore, informal security analysis demonstrates that our FedLAS scheme can resist multiple common attacks and meet predefined security requirements. Extensive comparative experiments show that the FedLAS scheme achieves excellent performance in computational and communication cost. It is well suitable for resource-constrained federated learning scenarios.