A Federated Graph Neural Network with Differential Privacy for Cross-domain Recommender Systems

Cross-domain recommender systems, which are designed to address issues with data sparsity, tend to suffer notable challenges with safeguarding user privacy. While existing cross-domain recommendation methods incorporate privacy mechanisms, they often fall short in practice, offering only one-sided benefits and limited privacy safeguards. In this study, we propose a novel privacy-preserving cross-domain recommender system that combines federated transfer learning with differential privacy to facilitate cross-domain knowledge transfer while ensuring strong privacy protection. First, we leverage federated transfer learning, treating each domain as an independent client to protect privacy for business partners by preventing the exchange of raw data. Second, we use a graph neural network (GNN) as the encoder to learn the user and item representations. We also design a consistency loss function that maintains the invariance between local and global user representations while preventing representation collapse. Third, we introduce a privacy mechanism that applies differential privacy to the output of each aggregation layer in the GNN—the aim being to protect transferred user representations while balancing privacy with accuracy. Finally, our transfer mechanism operates without user-identifying information, establishing connections between domains by detecting latent overlapping users and subsequently performing personalized preference aggregation. This allows for efficient knowledge transfer across domains. Experiments on real-world datasets show that our approach significantly enhances recommendation accuracy while offering robust privacy protection, outperforming state-of-the-art baselines.