Abstract
The rapid proliferation of Artificial Intelligence of Things (AIoT) devices in smart cities, such as roadside sensors and traffic cameras, enables real-time urban traffic monitoring through distributed sensor networks. These systems generate spatio-temporal data critical for Intelligent Transportation Systems (ITS), particularly traffic forecasting, and prediction of congestion, accidents, and travel times. However, existing forecasting methods struggle with cross-city collaboration due to heterogeneous sensor topologies and privacy constraints, limiting their effectiveness. Federated Learning partially mitigates privacy issues but fails to effectively address the topological heterogeneity of sensor networks, impeding robust cross-city collaboration and generalization. To address these limitations, we propose FedGraphX, a Split Federated Graph Learning framework that integrates localized processing with global collaboration. Each city operates as an independent AIoT node, using GRU-based encoders and GraphSAGE models to extract spatio-temporal features from local data. A central Graph Transformer aggregates features across cities, linking sensors via temporal and functional similarities, while a cross-layer attention mechanism aligns local spatial patterns with global dynamics. Experiments on four real-world datasets demonstrate superiority of FedGraphX over centralized and federated baselines, particularly in scenarios with sparse data or topological mismatches.