Enhancing federated learning-based social recommendations with graph attention networks

Social recommendations are crucial in information services, particularly for mitigating information overload on the Internet. Graph Attention Networks (GATs) have emerged as an effective solution for the social recommendation problem, harnessing the benefits of representation learning from user-item and user-user graphs. However, traditional GAT-based social recommendation models often necessitate the centralized storage and aggregation of all the users' social connections and item interactions. This centralization raises significant privacy concerns. To address these issues, we propose a federated learning-based social recommendation system using GATs. Specifically, we first design a structured-enhanced graph clustering algorithm to obtain soft clustering assignments for items. Based on these assignments, the similarity between pairwise items and their corresponding correlations are computed. Second, we employ graph attention networks to infer the local heterogeneity of social relationships, user-item interactions, and item relationships within the federated learning framework for each client. Using the soft clustering assignments, we derive hard clustering indicators and sample pseudo items. Local gradients of embedding and model parameters are then computed and uploaded to the central server for aggregation under the local differential privacy mechanism. Experimental results over real-world data sets indicate that the proposed model is more effective in privacy protection while maintaining acceptable performance compared with the central learning framework. Additionally, the proposed model is more stable in addressing the cold-start issue for items than state-of-the-art federated learning-based social recommendation models.