Edge‐Driven Federated Learning Approach for Distributed Assault Monitoring in Vehicular Networks

ABSTRACT Vehicular networks are essential to the advancement of intelligent transportation systems by enabling continuous data exchange between vehicles and infrastructure to support safe mobility. However, the distributed and dynamic nature of these networks creates opportunities for adversarial threats. Existing attack‐detection models primarily rely on centralized architectures, which often suffer from high latency, privacy risks, and limited robustness against advanced attacks. To address these challenges, this study proposes the LIFT‐SFD model. This lightweight federated learning framework integrates Smooth Federated Dropout (SFD) with trust‐weighted mask‐aware aggregation for secure and resource‐aware training in vehicular ad hoc networks (VANET). Each vehicle trains a masked submodel, while smooth dropout regularization ensures stable convergence and reduced communication overhead. Then, an integrated assault monitoring module detects and reduces malicious behavior by assigning anomaly scores at the vehicle level, adjusting trust weights during RSU‐level aggregation, and gradually filtering out malicious participants. The simulation of the models is performed using the VeReMi dataset, demonstrating high detection accuracy of 99.98% at round 5. It acts as a stable and trustworthy global model for future vehicular networks.