FedGST: An Efficient Federated Graph Neural Network for Spatio-temporal PoI Recommendation

Abstract

With the proliferation of sensor networks in urban areas, vast amounts of data from location-based social network platforms are now available, thus enabling the stakeholders to develop location-aware services for the end users. PoI (Point of Interests) recommendation is one of the most trending services. PoI recommendation models are developed by considering geographical information and historical records. More recently, with the demand for achieving PoI recommendations on mobile devices, embedded devices, or Internet of Things (IoT) devices, the challenges posed by the limited computational resources, malicious clients, and data silos, the model performance gets immensely affected. To address the above challenges and achieve efficient PoI recommendations, we propose an efficient federated graph learning-based model for mining complex spatiotemporal features to generate recommendations. We implement a GRU-based encoder-decoder to learn the temporal hidden state embeddings. Simultaneously, a 2-layer graph network is used to understand spatial embedding vectors. We also introduce an efficient contribution evaluation method to speed up the training process and improve the recommendation performance. Experiments on the PoI recommendation task based on real-life check-in data validate the effectiveness of our proposed model, and the results indicate that our recommendation model can achieve competitive results with lower computational costs.