Federated Cross-Domain Recommendation Framework With Graph Neural Network

Abstract

Cross-domain recommendation (CDR) leverages more abundant source-domain information to improve target-domain recommendation accuracy. However, traditional centralized CDR approaches face two critical limitations: (1) centralized data storage causes privacy vulnerabilities against malicious servers, and (2) gradient leakage during uploading enables recovery of source data. To address these challenges, in this work, we propose FedGraphCDR, a federated learning-based cross-domain recommendation framework that integrates local differential privacy (LDP) with pseudo item injection during gradient aggregation to prevent gradient leakage attacks, while utilizing graph neural networks to identify comparable users and mitigate cold-start problems. Evaluation on a real-life Douban dataset spanning three domains demonstrates that our framework successfully combines LDP with pseudo items to enhance privacy protection while achieving superior recommendation accuracy over benchmark methods. The results confirm that FedGraphCDR effectively resolves privacy concerns and improves recommendation quality, particularly for cold-start users, and establishes a practical solution for privacy-preserving cross-domain recommendation.