FedHGS: A Federated Point-of-Interest Recommendation Method Based on Heterogeneous Graph Semantics

Federated point-of-interest (POI) recommendation achieves global model training and optimization through a distributed training framework, ensuring that user data is always stored locally on the client side, thereby effectively protecting user data privacy. However, this distributed nature of data also severely limits the performance improvement of POI recommendation models under federated learning. On the one hand, in such a distributed data environment, the semantic fragmentation of feature spaces in local client data hinders the learning of user personalized preference representations, thereby limiting the improvement of local model personalization performance. On the other hand, the heterogeneity in data quantity, quality, and distribution among clients leads to significant differences in local model training.The traditional average aggregation strategy is difficult to effectively alleviate this difference, affecting the global aggregation efficiency and resulting in insufficient performance of the global model. To address these challenges, this paper proposes a federated POI recommendation method based on heterogeneous graph semantics (FedHGS). This method enhances the representation of local user preferences and improves the personalization of local models by learning personalized user preference features through heterogeneous graph semantic mining. Additionally, it introduces local model distillation alignment and performance-aware aggregation to balance training differences across clients and improve the performance of the global POI recommendation model. Finally, extensive experiments are conducted to verify both the global and local performance advantages of FedHGS.