Federated graph learning with spatio-temporal dynamics for cross-border recommendation.

Cross-border data sharing is strictly constrained by privacy regulations, which presents a critical challenge for recommendation systems due to the severe shortage of training data. Existing federated graph neural network methods predominantly rely on the federated averaging strategy, which struggles to handle the highly heterogeneous data encountered in scenarios characterized by user isolation and business homogeneity. To address this issue, this paper proposes FedSTAR, a privacy-preserving cross-border recommendation framework that integrates spatio-temporal dynamic modeling with federated graph neural networks. Its core innovations include the design of a dynamic sequential graph structure to capture the evolution of user preferences, the use of a multi-head attention mechanism to filter noisy neighbors in the spatial dimension, and the introduction of a personalized federated aggregation strategy to replace traditional FedAvg, thereby enabling adaptive fusion of heterogeneous multi-source data. Evaluations on three public datasets, Gowalla, Yelp 2018, and Amazon Book, demonstrate that FedSTAR achieves average improvements of 2-5 percentage points in Recall@20 and 1-3 percentage points in NDCG@20, respectively. Under the privacy constraint of exchanging only model updates, FedSTAR delivers both high accuracy and strong robustness, offering a secure and practically viable solution for cross-border recommendation.