FedTULGAC: A Federated Learning Method for Trajectory User Linking Based on Graph Attention and Clustering

Trajectory User Linking (TUL) is a pivotal technology for identifying and associating the trajectory information from the same user across various data sources. To address the privacy leakage challenges inherent in traditional TUL methods, this study introduces a novel federated learning-based TUL method: FedTULGAC. This approach utilizes a federated learning framework to aggregate model parameters, thereby avoiding the sharing of local data. Within this framework, a Graph Attention-based Trajectory User Linking and Embedding Regression (GATULER) model and an FL-DBSCAN clustering algorithm are designed and integrated to capture short-term temporal dependencies in user movement trajectories and to handle the non-independent and identically distributed (Non-IID) characteristics of client-side data. Experimental results on the synthesized datasets demonstrate that the proposed method achieves the highest prediction accuracy compared to the baseline models and maintains stable performance with minimal sensitivity to variations in client selection ratios, which reveals its effectiveness in bandwidth-constrained real-world applications.