A Multi-Layer Privacy-Preserving Federated AI Framework for Smart Vehicle Cybersecurity

Abstract

Connected and automated vehicles rely on V2X communication, edge devices, cloud services, and onboard sensors, creating large attack surfaces and privacy challenges for conventional centralized intrusion detection systems. This study proposes and evaluates a multi-layer privacy-preserving federated AI framework for smart vehicle cybersecurity. The framework integrates in-vehicle anomaly detection, cloud-based threat correlation, and federated learning to enable collaborative model training without exchanging raw vehicular telemetry data. A hybrid experimental testbed combining NVIDIA Jetson Nano edge nodes, the Flower federated learning framework, PyTorch-based detection models, SUMO mobility simulation, and NS-3 vehicular communication modeling was used to evaluate detection performance, latency, scalability, privacy preservation, attack surface coverage, and adversarial robustness. The results show an F1-score of 0.97, inference latency below 50 ms, 89% robustness under FGSM-based adversarial perturbations, and approximately 14% CPU overhead within the evaluated fleet-size settings. Compared with traditional IDS and cloud-only detection, the proposed framework improves privacy preservation and scalability while maintaining real-time response capability. Blockchain and quantum cryptography are discussed only as potential future research directions and were not experimentally implemented or validated. These findings indicate that federated AI can provide a scalable, privacy-aware, and resilient foundation for securing next-generation smart vehicle environments.