Federated Learning-Based Frameworks for Trusted and Secure Communication in IoVs

Federated learning is a machine learning approach that allows many parties to collaborate on training a model without disclosing their raw data. Federated learning is critical in the context of the Internet of Vehicles (IoVs) because it allows cars to exchange sensitive data while maintaining privacy and security. This chapter of the book delves into federated learning-based frameworks for trustworthy and secure communication in IoVs. The chapter investigates the difficulties associated with training machine learning models in IoVs and evaluates the various federated learning frameworks offered for this context. The chapter examines the significance of secure communication and privacy protection in federated learning and the many strategies and procedures utilized to achieve these objectives. It investigates federated learning's possible applications in IoVs, such as traffic prediction and management, intelligent routing optimization, and vehicle safety and security enhancement. Finally, the chapter discusses future research areas for federated learning in IoVs and their implications for the discipline. While numerous federated learning frameworks have been developed for IoVs, privacy and security issues must be solved before federated learning can realize its full potential in IoVs. The chapter suggests several potential future research areas, including developing new federated learning frameworks that better address the challenges of IoVs, exploring additional federated learning applications in this context, and evaluating the performance and efficiency of different federated learning approaches in IoVs.

Keywords: Federated learning, IoVs, Machine learning, Privacy preservation, Secure communications.