Federated Subgraph Learning over Spatio-Temporal Graphs for Traffic Prediction

The advancement of federated learning (FL) has enabled model training in spatio-temporal graphs (STG) while addressing privacy concerns that restrict direct data sharing. However, capturing spatio-temporal dependencies in FL settings that involve subgraphs remains challenging. On one hand, nodes in a subgraph struggle to model spatio-temporal correlations across different subgraphs. On the other hand, temporal data (e.g., traffic flows) often exhibits distinct non-i.i.d. patterns across subgraphs. To address these challenges, we propose a novel framework called Federated Subgraph Learning over Spatio-Temporal Graphs (FedSTG) for effective traffic prediction. FedSTG leverages attention-based models and includes two key modules. First, the Isolated Nodes Connection module integrates and filters the cluster centers of node embeddings from other subgraphs to enhance local model training, thereby capturing spatio-temporal correlations between nodes across subgraphs. Second, the Personalized Model Aggregation module compute the similarities between clients pairwise using their local models and aggregates personalized models based on the similarities as aggregation weights, thereby ensuring the effective model aggregation. Extensive experiments on two attention-based models and three commonly used traffic prediction datasets demonstrate the superiority of FedSTG.