Predicting Traffic Flow with Federated Learning and Graph Neural with Asynchronous Computations Network

arXiv:2401.02723v2 Announce Type: replace
Abstract: Real-time traffic flow prediction holds significant importance within the domain of Intelligent Transportation Systems (ITS). The task of achieving a balance between prediction precision and computational efficiency presents a significant challenge. In this article, we present a novel deep-learning method called Federated Learning and Asynchronous Graph Convolutional Network (FLAGCN). Our framework incorporates the principles of asynchronous graph convolutional networks with federated learning to enhance the accuracy and efficiency of real-time traffic flow prediction. The FLAGCN …

abstract article arxiv asynchronous balance challenge computational cs.cv cs.lg domain efficiency federated learning flow graph importance intelligent intelligent transportation network novel precision prediction real-time systems traffic transportation type