Technology Category
Machine Learning: Distributed Machine Learning & Federated LearningPlanning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsKnowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
Unlocking Dynamic Inter-Client Spatial Dependencies: A Federated Spatio-Temporal Graph Learning Method for Traffic Flow Forecasting
🤖 AI Summary
To address the challenges of modeling dynamic cross-client spatial dependencies and balancing privacy with predictive performance in traffic flow forecasting under federated learning, this paper proposes FedSTGD—a novel federated spatio-temporal graph learning framework. FedSTGD is the first to explicitly model dynamic spatial dependencies in a federated setting. It introduces a nonlinear graph computation decomposition mechanism and a node embedding enhancement module to decouple complex graph operations and strengthen local representation capability. Additionally, it establishes a lightweight server–client coordination protocol to enable efficient distributed spatio-temporal graph learning. Extensive experiments on four real-world traffic datasets demonstrate that FedSTGD consistently outperforms existing state-of-the-art methods in RMSE, MAE, and MAPE, achieving performance close to centralized training. Ablation studies validate the effectiveness of each component, while hyperparameter analysis confirms strong robustness.
Technology Category
Application Category
📝 Abstract
Spatio-temporal graphs are powerful tools for modeling complex dependencies in traffic time series. However, the distributed nature of real-world traffic data across multiple stakeholders poses significant challenges in modeling and reconstructing inter-client spatial dependencies while adhering to data locality constraints. Existing methods primarily address static dependencies, overlooking their dynamic nature and resulting in suboptimal performance. In response, we propose Federated Spatio-Temporal Graph with Dynamic Inter-Client Dependencies (FedSTGD), a framework designed to model and reconstruct dynamic inter-client spatial dependencies in federated learning. FedSTGD incorporates a federated nonlinear computation decomposition module to approximate complex graph operations. This is complemented by a graph node embedding augmentation module, which alleviates performance degradation arising from the decomposition. These modules are coordinated through a client-server collective learning protocol, which decomposes dynamic inter-client spatial dependency learning tasks into lightweight, parallelizable subtasks. Extensive experiments on four real-world datasets demonstrate that FedSTGD achieves superior performance over state-of-the-art baselines in terms of RMSE, MAE, and MAPE, approaching that of centralized baselines. Ablation studies confirm the contribution of each module in addressing dynamic inter-client spatial dependencies, while sensitivity analysis highlights the robustness of FedSTGD to variations in hyperparameters.
Problem
Research questions and friction points this paper is trying to address.
Modeling dynamic spatial dependencies in federated traffic forecasting
Overcoming data locality constraints for distributed traffic data
Reconstructing inter-client dependencies without centralized data access
Innovation
Methods, ideas, or system contributions that make the work stand out.
Models dynamic inter-client dependencies in federated learning
Uses nonlinear decomposition for complex graph operations
Employs embedding augmentation to reduce performance degradation
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