Federated Knowledge-enhanced Graph Attention Network for Privacy-preserving Social Recommendation

Graph neural networks (GNNs) are widely utilized in recommender systems to model higher-order interactions between users and items due to their capacity to handle the intricate relational data. Federated Learning, known for its excellent privacy-preserving capabilities, is extensively applied in recommender systems. Nonetheless, existing GNN-based recommendation methods fail to fully leverage the integration of social networks and knowledge graphs into the recommendation process, neglecting the subtle differences between these data sources. Furthermore, current federated learning-based recommendation frameworks cannot balance efficiency and privacy protection, with adversaries potentially exploiting attacks to breach user privacy. To address these issues, we propose Federated Knowledge-enhanced Graph Attention Network for Social Recommendation (FGAT4SR) to achieve efficient recommendations. Specifically, we use graph attention networks to learn the rich knowledge and aggregate their most critical information. Then pseudo-item interactions and local differential privacy are incorporated into our model to ensure privacy protection. Extensive experiments on real datasets demonstrate that our proposed federated recommendation framework outperforms all baseline methods. Our approach enables the sophisticated integration of social relationships and knowledge graphs while preserving privacy.