Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles (CAV), including perception, planning, and control. However, its reliance on vehicular data for model training presents significant challenges related to in-vehicle user privacy and communication overhead generated by massive data volumes. Federated learning (FL) is a decentralized ML approach that enables multiple vehicles to collaboratively develop models, broadening learning from various driving environments, enhancing overall performance, and simultaneously securing local vehicle data privacy and security. This survey paper presents a review of the advancements made in the application of FL for CAV (FL4CAV). First, centralized and decentralized frameworks of FL are analyzed, highlighting their key characteristics and methodologies. Second, diverse data sources, models, and data security techniques relevant to FL in CAVs are reviewed, emphasizing their significance in ensuring privacy and confidentiality. Third, specific and important applications of FL are explored, providing insight into the base models and datasets employed for each application. Finally, existing challenges for FL4CAV are listed and potential directions for future work are discussed to further enhance the effectiveness and efficiency of FL in the context of CAV.

翻译：机器学习（ML）广泛用于网联自动驾驶（CAV）的关键任务，包括感知、规划和控制。然而，其对车辆数据训练模型的依赖带来了与车内用户隐私和大量数据产生的通信开销相关的重大挑战。联邦学习（FL）是一种去中心化的ML方法，使多辆车能够协作开发模型，从多种驾驶环境中扩展学习，提升整体性能，同时保障本地车辆数据的隐私与安全。本综述论文回顾了FL在CAV（FL4CAV）应用中的研究进展。首先，分析了FL的集中式和分布式框架，突出了其关键特征和方法。其次，综述了与CAV中FL相关的多种数据来源、模型和数据安全技术，强调了其在确保隐私和保密性方面的重要性。第三，探讨了FL的具体重要应用，揭示了每项应用所使用的基础模型和数据集。最后，列出了FL4CAV当前面临的挑战，并讨论了未来研究的方向，以进一步提升FL在CAV场景中的有效性和效率。