A Survey on Federated Learning in Intelligent Transportation Systems
arxiv(2024)
摘要
The development of Intelligent Transportation System (ITS) has brought about
comprehensive urban traffic information that not only provides convenience to
urban residents in their daily lives but also enhances the efficiency of urban
road usage, leading to a more harmonious and sustainable urban life. Typical
scenarios in ITS mainly include traffic flow prediction, traffic target
recognition, and vehicular edge computing. However, most current ITS
applications rely on a centralized training approach where users upload source
data to a cloud server with high computing power for management and centralized
training. This approach has limitations such as poor real-time performance,
data silos, and difficulty in guaranteeing data privacy. To address these
limitations, federated learning (FL) has been proposed as a promising solution.
In this paper, we present a comprehensive review of the application of FL in
ITS, with a particular focus on three key scenarios: traffic flow prediction,
traffic target recognition, and vehicular edge computing. For each scenario, we
provide an in-depth analysis of its key characteristics, current challenges,
and specific manners in which FL is leveraged. Moreover, we discuss the
benefits that FL can offer as a potential solution to the limitations of the
centralized training approach currently used in ITS applications.
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