Federated Learning-Based Data Dissemination Systems for IoVs

Federated learning-based data dissemination solutions for Internet of Vehicles (IoVs) are gaining interest owing to their capacity to increase data dissemination performance and privacy. This chapter examines the current state of the art in federated learning-based data dissemination systems for IoVs, as well as the obstacles and possibilities associated with their deployment. A literature study, analysis of data dissemination needs in IoVs, and assessment of performance and privacy implications of alternative federated learning techniques are all part of the process for creating and assessing federated learning-based data dissemination systems in IoVs. The findings of a literature analysis and tests evaluating the performance and privacy of federated learning-based data dissemination systems in IoVs reveal that these systems have the potential to increase data dissemination performance and privacy, but various problems must be addressed. This chapter adds to the current literature by offering a thorough examination of the state-of-the-art federated learning-based data distribution systems for IoVs. The chapter discusses important obstacles and possibilities, as well as insights into the approach used to create and evaluate these systems. The chapter explores the consequences for IoVs of federated learning-based data dissemination systems, such as better data dissemination performance and privacy. The chapter focuses on possible applications in smart transportation, urban planning, and public safety. The chapter investigates the implications of federated learning-based data dissemination systems for IoVs, such as improved data dissemination performance and privacy. The chapter focuses on smart transportation, urban planning, and public safety applications. 

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