PPSTSL: A Privacy-preserving Dynamic Spatio-temporal Graph Data Federated Split Learning for traffic forecasting

Abstract

In intelligent transportation, federated learning has garnered significant attention for its privacy protection and model optimization capabilities. However, existing approaches still struggle with high computational and communication costs. Moreover, challenges such as the uneven distribution of traffic nodes and time sequences, global dynamic spatio-temporal correlation, and security concerns in the spatio-temporal modeling process remain insufficiently addressed in distributed transportation research. To tackle these challenges, this study proposed a Privacy-Preserving Dynamic Spatio-temporal Graph Data Federated Split Learning (PPSTSL) method. By incorporating a split learning framework for spatio-temporal modeling under privacy protection, PPSTSL enables the integration of multi-client data to enhance global model performance while mitigating computational and communication overhead. Specifically, we present Federated Distribution-Aligned Temporal Dependency Modeling (FedDATDep) to optimize the model parameters and enrich temporal features. Additionally, we design Federated Spatio-Temporal Fusion (FedTSFus) to achieve global dynamic spatio-temporal dependencies while preserving privacy. Furthermore, we propose a Positive-Negative Coupled Coding (PNCC) mechanism to enhance the computational and communication efficiency of the introduced security techniques. Experimental results show that PPSTSL achieves superior model performance in both independent and non-independent identical distribution scenarios (IID and Non-IID) on the SZ-Taxi and Los-Loop traffic datasets, while demonstrating strong scalability. Furthermore, the optimized security techniques not only enhance privacy protection but also reduce communication and computational overhead. The proposed PPSTSL approach addresses crucial limitations in existing distributed research methods in intelligent transportation, presenting a promising direction for future research and practical implementations.