Blockchain-Driven Distributed Edge Intelligence for Enhanced Internet-of-Vehicles

In the evolving landscape of vehicular networks, it is crucial to ensure robust security and efficient data handling. In this work, We introduce a novel federated learning(FL) algorithm integrated within a Distributed Edge Intelligence (DEI) framework, enhanced by a blockchain consensus mechanism, specifically designed for Internet-of-Vehicles (IoV) to enhance data privacy, efficiency, and system resilience. Motivated by the pressing need for improved data privacy and security in the Internet of Vehicles (IoVs), our approach can not only prioritize these aspects but also enhance the efficiency and accuracy of distributed machine learning. The proposed consensus mechanism, by integrating Proof-of-Knowledge (PoK) with Practical Byzantine Fault Tolerance (PBFT), is crafted to be lightweight, making it suitable for the dynamic and resource-constrained vehicular environments. Our evaluation findings demonstrate the algorithm's superior performance and scalability, suggesting its applicability in diverse IoV scenarios and its potential to facilitate secure, robust, and efficient collaborative learning.