Federated Spatio-Temporal Traffic Flow Prediction Based on Graph Convolutional Network

In recent years, traffic flow prediction has attracted increasing interest from both academia and industry, and existing data-driven learning models for traffic flow prediction have achieved excellent success. However, this requires a large number of datasets for efficient model training, while it is difficult to acquire all the data from one agent, and thus data collaboration among different agents becomes an attracting trend. Moreover, with the increase in the number of agents, how to perform accurate multi-agent traffic forecasting while protecting privacy is an important issue. To address this challenge, we introduce a privacy-preserving federated learning framework. In this paper, we propose a novel Dynamic Spatio-Temporal traffic flow prediction model based on graph convolutional network (DST-GCN), which incorporates both dynamic spatial and temporal dependence of intersection traffic. In addition, we provide an improved federated learning framework with opportunistic client selection (FLoS). In the proposed FLoS protocol, we employ a FedAVG algorithm for secure parameter aggregation and design an optimal client selection algorithm to reduce the communication overhead during the transfer of model updates. Experiments based on real-world datasets demonstrate that our proposed DST-GCN traffic prediction model outperforms state-of-the-art baseline models. And our proposed FLoS can achieve superior results while reducing communication consumption.