https://jjcit.org/paper/279 10.5455/jjcit.71-1752597738 Moawiah El-Dalahmeh,Adi El-Dalahmeh Federated learning,VANET,Intrusion-detection system,Cybersecurity,Distributed AI,Privacy preservation,Edge computing 2063 609 15-Jul.-2025 8-Nov.-2025 9-Nov.-2025 Vehicular Ad Hoc Networks (VANETs) are a cornerstone of modern Intelligent Transportation Systems (ITSs), enabling real-time communication among vehicles and infrastructure. However, the open and dynamic nature of VANETs exposes them to a wide range of cybersecurity threats, such as spoofing, Sybil attacks and denial-of- service (DoS). This paper introduces a novel Federated Learning (FL) framework designed to enhance VANET security by enabling distributed and privacy-preserving intrusion detection across the network. By leveraging local model updates instead of centralized data aggregation, our proposed FL approach mitigates privacy risks, reduces communication overhead and offers robust detection of cyber-threats. The paper presents a comprehensive analysis including system architecture, threat modeling, security properties, performance evaluation and real-world applicability. Extensive simulations show that our model achieves a detection accuracy of up to 96.2%, with minimal latency and low model convergence time, outperforming existing centralized and traditional machine-learning models.