Semi-supervised Federated Learning for Misbehavior Detection of BSMs in Vehicular Networks

Basic Safety Messages (BSMs) exchanged among vehicles and roadside units through vehicular communications can significantly enhance road safety and improve traffic efficiency. Protecting the integrity of BSMs, which are transmitted wirelessly in plaintext, is critical for the proper operation of vehicular networks. As a result, various machine learning-based misbehavior detection systems have been proposed to identify corrupted BSMs. Recent studies have applied federated learning methods to further preserve user privacy while facilitating detection model updates. However, supervised federated learning cannot be directly applied since BSMs received by vehicles are unlabeled. In this paper, we propose a semi-supervised federated learning framework that enables the federated training process between vehicles and the server without transmitting any datasets. Our experimental results show that the performance of the proposed semi-supervised framework is very close to the centralized method, while preserving user privacy and reducing communication costs.