SCFL: Spatio-temporal consistency federated learning for next POI recommendation

Existing personalized federated learning frameworks fail to significantly improve the personalization of user preference learning in next Point-Of-Interest (POI) recommendations, causing notable performance deficits. These frameworks do not fully consider crucial factors such as: (1) how to thoroughly explore spatial–temporal relationships within user trajectories to deeply understand personalized behavior patterns, and (2) the neglect of collaborative signals among users with similar spatio-temporal distributions, which results in the loss of valuable shared information. To tackle these challenges, this paper introduces the Spatio-temporal Consistency Federated Learning (SCFL) framework, which capitalizes on the spatio-temporal consistency of trajectories to boost the personalized performance of POI recommendation models in FL. Specifically, we have developed the trajectory optimization module SCA for clients in isolation to extract deeper behavioral patterns from the spatio-temporal distribution of sparse trajectories. Additionally, we present a hierarchical aggregation strategy based on distribution consistency, utilizing intermediate entities called Edges to aggregate similar users, thereby enhancing the model’s learning of shared information. Experimental validation across three real-world datasets (NYC, TKY and Gowalla) and two models (SASRec and SSEPT) with six scalability settings shows that SCFL substantially outperforms eight strong baselines. In six experimental configurations, SCFL achieves a personalized performance improvement of 10.65% over the best baselines. Additional experiments have also validated the superiority of SCFL from various perspectives.