Xinran Zhang, Jingyuan Liu, T. Hu, Zheng Chang, Yanru Zhang, Geyong Min
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引用次数: 0
Abstract
Recently, realizing machine learning (ML)-based technologies with the aid of mobile edge computing (MEC) in the vehicular network to establish an intelligent transportation system (ITS) has gained considerable interest. To fully utilize the data and onboard units of vehicles, it is possible to implement federated learning (FL), which can locally train the model and centrally aggregate the results, in the vehicular edge computing (VEC) system for a vision of connected and autonomous vehicles. In this article, we review and present the concept of FL and introduce a general architecture of FL-assisted VEC to advance development of FL in the vehicular network. The enabling technologies for designing such a system are discussed and, with a focus on the vehicle selection algorithm, performance evaluations are conducted. Recommendations on future research directions are highlighted as well.
期刊介绍:
IEEE Vehicular Technology Magazine is a premier publication that features peer-reviewed articles showcasing advancements in areas of interest to the IEEE Vehicular Technology Society. Our scope encompasses theoretical, experimental, application, and operational aspects of electrical and electronic engineering relevant to motor vehicles and associated land transportation infrastructure. This includes technologies for terrestrial mobile vehicular services, components, systems, and auxiliary functions within motor vehicles, as well as components and systems used in both automated and non-automated facets of ground transport technology. The magazine focuses on intra-vehicular components, systems, and applications, offering tutorials, surveys, coverage of emerging technology, and serving as a platform for communication between the IEEE VTS governing body and its membership. Join us in exploring the latest developments in vehicular technology.