Role of Federated Learning for Internet of Vehicles: A Systematic Review

The Internet of Vehicles (IoV) is one of the most exciting and practical ways that corporations and academics are interested in, especially by employing coordinated unmanned vehicles to explore areas like the automobile industry. To provide long-term possibilities for task investigations, the IoV connects vehicles, transportation networks, and communication infrastructure. Data privacy, however, may be compromised by the coordination of information gathering from numerous sources. Federated Learning (FL) is the answer to these concerns of privacy, scalability, and high availability. A well-distributed learning framework designed for edge devices is federated learning. It makes use of large-scale processing from edge devices while allowing private data to remain locally. In this work, different categories of federated learning have been discussed. A review of various systems implementing FL for IoV has been presented followed by the applications and challenges of FL in the IoV paradigm. The paper concludes by providing future research directions for FL in the IoV.