Blockchain-Enhanced Federated Learning for Internet of Vehicles

Muhammad Asad, Zayed University
Safa Otoum, Zayed University

Document Type

Conference Proceeding

Source of Publication

2024 6th International Conference on Blockchain Computing and Applications (BCCA)

Publication Date

11-29-2024

Abstract

The integration of the Internet of Vehicles (IoV) into urban environments introduces significant data security and network efficiency challenges. As vehicular networks expand, their susceptibility to cyber-attacks increases, demanding effective security solutions. This paper proposes a novel framework by integrating blockchain technology with federated learning (FL) to secure and enhance the efficiency of IoV systems. In this context, we propose a Blockchain-Enhanced FL for Internet of Vehicles (BEFL-IoV) framework designed to optimize communication efficiency and protect data privacy within IoV networks. Our approach distributes data processing tasks across vehicles, reducing latency and network congestion while ensuring high data privacy and integrity. Blockchain provides a decentralized, tamper-resistant layer for secure transactions and data exchanges between vehicles and infrastructure. Simulation results confirm the framework’s efficacy in maintaining high data accuracy under various cyber threats. Specifically, the model achieves 0.94% accuracy against property inference attacks and 0.92% accuracy against membership inference attacks. This integration of blockchain and FL significantly improves the scalability and reliability of IoV applications, marking a major advancement in smart transportation technologies.

ISBN

979-8-3503-5153-8

Publisher

IEEE

Volume

00

First Page

704

Last Page

709

Disciplines

Computer Sciences

Keywords

Blockchain, Federated Learning, Internet of Vehicles, Data Privacy, Cyber Security

Indexed in Scopus

no

Open Access

no

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