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

TL;DR

This paper introduces FLAGCN, a novel federated learning framework with asynchronous graph neural networks for real-time traffic flow prediction, improving accuracy and efficiency over existing models.

Contribution

The paper presents a new deep learning framework combining asynchronous graph convolutional networks with federated learning for traffic prediction.

Findings

FLAGCN reduces RMSE by approximately 6.85%.

FLAGCN decreases MAPE by about 20.45%.

FLAGCN enhances training and inference efficiency.

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 model employs a spatial-temporal graph convolution technique to asynchronously address spatio-temporal dependencies within traffic data effectively. To efficiently handle the computational requirements associated with this deep learning model, this study used a graph federated learning technique known as GraphFL. This approach is designed to facilitate the training process. The experimental results obtained from conducting tests on two distinct traffic datasets demonstrate that the utilization of FLAGCN leads to the optimization of both training and inference durations while maintaining a high level of prediction accuracy. FLAGCN outperforms existing models with significant improvements by achieving up to approximately 6.85% reduction in RMSE, 20.45% reduction in MAPE, compared to the best-performing existing models.