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.
