Federated Learning for Intelligent Transportation Systems: Use Cases, Open Challenges, and Opportunities

Intelligent transportation systems (ITSs) leverage a network of interconnected infrastructures utilizing advanced technologies to improve traffic management and safety. Federated learning (FL) has emerged as a pivotal method within ITSs, enabling decentralized collaborative model training without direct data sharing, thus preserving privacy and enhancing system efficiency. This article explores the integration of FL in ITSs, focusing on FL's application in traffic flow prediction, trajectory prediction, parking space estimation, and traffic target recognition. Despite its potential, FL deployment faces challenges, including data heterogeneity, communication and bandwidth constraints, and resource limitations on edge devices. Addressing these challenges is crucial for realizing the full potential of FL in ITSs. This article provides a comprehensive survey of existing FL implementations in ITSs, discusses inherent challenges, and outlines future research directions aimed at overcoming these obstacles.