In modern vehicular systems, providing secure data processing with decentralized learning efficacy under limited computational resources and varying network conditions is challenging. This paper introduces an intelligent, effective, and secure learning model for the Internet of Vehicles (IoV) as a solution to the vulnerability of centralized architectures and the inefficiency of existing federated learning in adversarial environments. The Blockchain-Enabled Hierarchical Federated Variational Autoencoder Learning (BHFVAL) model uses a multilevel learning process on edge, fog, and cloud layers protected by a Reputation-Based Byzantine Fault Tolerance (RBFT) mechanism filtering out incorrect inputs during model aggregation. HFVAL is at its core, providing adaptive encoding and learning task assignments based on dynamic networks and resource status. To minimize communication latency, the platform employs a lightweight edge-computing (LEC) module to enable proximity-based processing. Hyperparameter optimization is enabled using the Osprey Optimization Algorithm (OOA) for maximum convergence effectiveness. Secure communication is achieved by implementing a Lightweight Secure Communication Protocol (LSCP) on Elliptic Curve-Based Homomorphic Encryption (ECHE) to enable encrypted V2X communication with minimal computational overhead and reduced latency. Extensive experimentation using the UNSW-NB15 and CIC-IDS-2017 datasets exhibited strong detection performance: UNSW-NB15 achieved 96.83% accuracy and 96.65% F1-score under IID, slightly declining to 95.74% accuracy and 95. 40% F1-score under non-IID conditions. The CIC-IDS-2017 achieved 97.36% accuracy, 97.2% AUROC, and 97.1% F1-score under IID, slightly declining to 96.40% accuracy and 96.20% F1-score under non-IID conditions. The results attest to the dependability, adaptability, and efficacy of the framework in decentralized privacy-sensitive vehicular networks.
Sayeed et al. (Mon,) studied this question.