Federated learning (FL) is a distributed machine learning approach that can achieve the purpose of collaborative learning from a large amount of data that belong to different parties without sharing the raw data among the data owners. FL can sufficiently utilize the computing capabilities of multiple learning agents to improve the learning efficiency while providing a better privacy solution for the data owners. FL attracts tremendous interests from a large number of industries due to growing privacy concerns. Future vehicular Internet of Things (IoT) systems, such as cooperative autonomous driving and intelligent transport systems (ITS), feature a large number of devices and privacy-sensitive data where the communication, computing, and storage resources must be efficiently utilized. FL could be a promising approach to solve these existing challenges. In this paper, we first conduct a brief survey of existing studies on FL and its use in wireless IoT. Then we discuss the significance and technical challenges of applying FL in vehicular IoT, and point out future research directions.

中文翻译：

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车辆物联网联合学习：最新进展和未解决的问题。


联邦学习（FL）是一种分布式机器学习方法，可以从属于不同各方的大量数据中实现协作学习的目的，而无需在数据所有者之间共享原始数据。 FL可以充分利用多个学习代理的计算能力来提高学习效率，同时为数据所有者提供更好的隐私解决方案。由于日益增长的隐私问题，FL 吸引了许多行业的巨大兴趣。未来的车辆物联网（IoT）系统，例如协同自动驾驶和智能交通系统（ITS），具有大量设备和隐私敏感数据，必须有效利用通信、计算和存储资源。 FL 可能是解决这些现有挑战的一种有前途的方法。在本文中，我们首先对 FL 的现有研究及其在无线物联网中的应用进行了简要调查。然后我们讨论了将FL应用于车辆物联网的意义和技术挑战，并指出了未来的研究方向。