A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehicles

TL;DR

This paper systematically reviews how blockchain technology enhances federated learning frameworks to improve data privacy and security in Internet of Vehicles systems, discussing challenges, solutions, and future research directions.

Contribution

It provides a comprehensive survey of blockchain-enabled federated learning frameworks specifically designed for IoVs, highlighting current applications, issues, and potential future developments.

Findings

Blockchain enhances data security in IoVs.

Federated Learning preserves user privacy effectively.

Identifies key challenges and future research directions.

Abstract

While the convergence of Artificial Intelligence (AI) techniques with improved information technology systems ensured enormous benefits to the Internet of Vehicles (IoVs) systems, it also introduced an increased amount of security and privacy threats. To ensure the security of IoVs data, privacy preservation methodologies have gained significant attention in the literature. However, these strategies also need specific adjustments and modifications to cope with the advances in IoVs design. In the interim, Federated Learning (FL) has been proven as an emerging idea to protect IoVs data privacy and security. On the other hand, Blockchain technology is showing prominent possibilities with secured, dispersed, and auditable data recording and sharing schemes. In this paper, we present a comprehensive survey on the application and implementation of Blockchain-Enabled Federated Learning frameworks for IoVs. Besides, probable issues, challenges, solutions, and future research directions for BC-Enabled FL frameworks for IoVs are also presented. This survey can further be used as the basis for developing modern BC-Enabled FL solutions to resolve different data privacy issues and scenarios of IoVs.