The paper provides a comprehensive review of Federated Learning (FL) in Intelligent Transportation Systems (ITS), focusing on three key scenarios: traffic flow prediction, traffic target recognition, and vehicular edge computing. It highlights the limitations of centralized training approaches, such as poor real-time performance, data silos, and data privacy concerns, and discusses how FL addresses these issues. The paper details the characteristics, challenges, and specific applications of FL in each scenario, emphasizing its benefits in enhancing data privacy, improving real-time performance, and enabling efficient collaborative learning. It also explores various techniques to optimize communication efficiency, reduce communication overhead, and improve resource allocation in FL, particularly in vehicular edge computing environments. The review underscores the potential of FL to revolutionize ITS by providing a more secure, efficient, and privacy-preserving solution for traffic management and intelligent transportation systems.The paper provides a comprehensive review of Federated Learning (FL) in Intelligent Transportation Systems (ITS), focusing on three key scenarios: traffic flow prediction, traffic target recognition, and vehicular edge computing. It highlights the limitations of centralized training approaches, such as poor real-time performance, data silos, and data privacy concerns, and discusses how FL addresses these issues. The paper details the characteristics, challenges, and specific applications of FL in each scenario, emphasizing its benefits in enhancing data privacy, improving real-time performance, and enabling efficient collaborative learning. It also explores various techniques to optimize communication efficiency, reduce communication overhead, and improve resource allocation in FL, particularly in vehicular edge computing environments. The review underscores the potential of FL to revolutionize ITS by providing a more secure, efficient, and privacy-preserving solution for traffic management and intelligent transportation systems.