Distributed Edge Intelligence Framework for Secure and Efficient Data Sharing in 6G-IoV

This paper introduces a distributed edge intelligence framework for secure and efficient data sharing in 6G-Internet-of-Vehicles (6G-IoV) environments. The approach integrates a hierarchical blockchain architecture with an innovative asynchronous federated learning algorithm to address the challenges of privacy preservation, communication efficiency, and scalability in 6G-IoV scenarios. The proposed framework employs a multi-layer structure comprising vehicles, roadside units, and cloud servers, each playing a distinct role in the distributed learning process. We present a comprehensive latency model that accounts for computation and communication aspects, considering the unique characteristics of vehicular networks. The asynchronous federated learning algorithm incorporates genetic algorithm-based resource optimization to adaptively allocate communication resources, significantly enhancing efficiency in heterogeneous 6G-IoV environments. Furthermore, we introduce the hierarchical edge intelligence consensus mechanism, a lightweight consensus protocol tailored for edge intelligence scenarios, which accelerates blockchain consensus while maintaining security. Simulations using MNIST and SVHN datasets demonstrate that the proposed framework outperforms state-of-the-art methods regarding model accuracy, communication efficiency, and privacy preservation.