PAFL: Parameter-Authentication Federated Learning for Internet of Vehicles

Federated learning is an emerging distributed learning paradigm which brings an efficient and privacy-preserving intelligent model for the Internet of Vehicles (IoV). Unfortunately, federated learning is vulnerable to abnormal model attacks as it is hard to authenticate model parameters. Abnormal local models may slow down the convergence rate, reduce the accuracy of global models, and even deliberately control the global model in the attackers' chosen way. Furthermore, an abnormal global model may deduce sensitive information about vehicles and hinder the execution of genuine tasks. Therefore, in this paper, we propose a parameter-authentication federated learning (PAFL) scheme that can protect privacy of vehicles, such as driving habits, and defend against abnormal model attacks simultaneously. Concretely, we equip the federated learning framework with the zero knowledge proof and Pedersen commitment to prove and authenticate the reliability of model parameters. Security and privacy analysis, as well as performance evaluation show that the PAFL scheme can successfully detect abnormal models with higher detection rate and achieve more secure global aggregation than existing representative schemes.