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 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 investigation to further enhance the effectiveness and efficiency of FL in the context of CAV are discussed.

翻译：机器学习广泛应用于网联自动驾驶汽车的关键任务，包括感知、规划与控制。然而，机器学习模型训练对车载数据的依赖带来了重大挑战，涉及车内用户隐私以及海量数据产生的通信开销。联邦学习是一种去中心化的机器学习方法，允许多辆车协同开发模型，从而扩展来自不同驾驶环境的学习能力，提升整体性能，同时保障本地车辆数据的隐私与安全。本综述论文回顾了联邦学习在网联自动驾驶汽车中应用的研究进展。首先，分析了联邦学习的集中式与去中心化框架，突出了它们的关键特征与方法。其次，综述了网联自动驾驶汽车中联邦学习相关的多种数据源、模型及数据安全技术，强调了它们在确保隐私与机密性方面的重要性。第三，探讨了联邦学习的具体应用，深入分析了每项应用所采用的基础模型与数据集。最后，列出了联邦学习在网联自动驾驶汽车应用中存在的现有挑战，并讨论了未来研究方向，以进一步提升联邦学习在网联自动驾驶汽车背景下的效能与效率。