AT‐SPNet: A Personalized Federated Spatio‐Temporal Modeling Method for Cross‐City Traffic Prediction

ABSTRACT For cross‐city traffic prediction, the significant heterogeneity of traffic data across cities and the requirement for privacy protection make it challenging for conventional centralized spatiotemporal graph modeling techniques to balance predictive performance and data security. Therefore, this paper proposes AT‐SPNet, a personalized federated spatiotemporal modeling approach specifically designed for cross‐city traffic prediction. This method decouples the spatiotemporal modeling paths through the construction of a shared temporal branch and a hidden local spatial branch, thereby mitigating the heterogeneity of cross‐city traffic data while preserving privacy. In the temporal branch, Gated Recurrent Units and a multi‐head attention mechanism are incorporated to capture temporal dependencies, and a Squeeze‐and‐Excitation module is employed to enhance the extraction of informative features. In the spatial branch, a Spatial Attention Fusion module based on a triple‐attention mechanism is designed to capture spatial features from multiple spatial perspectives, combined with static graph convolution and dynamic graph attention to construct a dual‐modal information fusion path. Furthermore, to alleviate the adverse effects of cross‐city data heterogeneity in federated training, a personalized federated learning strategy is introduced, which enables differentiated fusion of client spatial features without sharing raw data. Experiments on four real‐world traffic datasets demonstrate that AT‐SPNet outperforms existing methods in both prediction accuracy and cross‐city generalization, validating the effectiveness and practical applicability of the proposed approach for cross‐city traffic prediction.