Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of the vehicle networks, it is rather challenging to make timely and accurate decisions of vehicle behaviors. Moreover, in the presence of mobile wireless communications, the privacy and security of vehicle information are at constant risk. In this context, a new paradigm is urgently needed for various applications in dynamic vehicle environments. As a distributed machine learning technology, federated learning (FL) has received extensive attention due to its outstanding privacy protection properties and easy scalability. We conduct a comprehensive survey of the latest developments in FL for ITS. Specifically, we initially research the prevalent challenges in ITS and elucidate the motivations for applying FL from various perspectives. Subsequently, we review existing deployments of FL in ITS across various scenarios, and discuss specific potential issues in object recognition, traffic management, and service providing scenarios. Furthermore, we conduct a further analysis of the new challenges introduced by FL deployment and the inherent limitations that FL alone cannot fully address, including uneven data distribution, limited storage and computing power, and potential privacy and security concerns. We then examine the existing collaborative technologies that can help mitigate these challenges. Lastly, we discuss the open challenges that remain to be addressed in applying FL in ITS and propose several future research directions.

翻译：智能交通系统（ITS）得益于通信技术、传感器技术和物联网（IoT）的快速发展，取得了长足进步。然而，由于车辆网络的动态特性，对车辆行为做出及时准确的决策相当具有挑战性。此外，在移动无线通信环境下，车辆信息的隐私与安全始终面临风险。在此背景下，动态车辆环境中的各类应用亟需新范式。作为一种分布式机器学习技术，联邦学习（FL）因其突出的隐私保护特性和易扩展性而受到广泛关注。本文对FL在ITS中的最新发展进行了全面综述。具体而言，我们首先研究了ITS中普遍存在的挑战，并从不同角度阐明了应用FL的动机。随后，我们回顾了FL在ITS中多场景下的现有部署，并讨论了在目标识别、交通管理和服务提供场景中的具体潜在问题。此外，我们进一步分析了FL部署带来的新挑战以及FL本身无法完全解决的固有局限性，包括数据分布不均、存储和计算能力有限以及潜在的隐私安全问题。然后，我们审视了有助于缓解这些挑战的现有协同技术。最后，我们探讨了FL在ITS应用中尚待解决的开放挑战，并提出了若干未来研究方向。