Year: 2024
Type: article
Abstract: 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 IoV, 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.
Source: IEEE Internet of Things Journal
Authors: Xiaofu Chen, Weizhi Meng, Heyang Huang
Institutions: Technical University of Denmark, Lancaster University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4404056882