  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.
