The paper "Privacy-Preserving Cross-Domain Recommendation with Federated Graph Learning" addresses the challenge of cross-domain recommendation (CDR) in a privacy-preserving manner. The authors propose a novel federated graph learning approach called Privacy-Preserving Cross-Domain Recommendation (PPCDR) to capture users' preferences based on distributed multi-domain data without privacy leakage. PPCDR models both global and local user preferences, characterized by shared and domain-specific tastes, respectively. The main components of PPCDR include a graph transfer module for each domain to fuse global and local user preferences, and a federated update process that applies local differential privacy to collaboratively learn global user preferences. The method is evaluated on three real-world datasets and shows consistent improvements over single- and cross-domain baselines while effectively protecting domain privacy. The key contributions of the paper are the development of a decentralized federated learning framework for CDR and the introduction of PPCDR, which enhances privacy protection and recommendation performance.The paper "Privacy-Preserving Cross-Domain Recommendation with Federated Graph Learning" addresses the challenge of cross-domain recommendation (CDR) in a privacy-preserving manner. The authors propose a novel federated graph learning approach called Privacy-Preserving Cross-Domain Recommendation (PPCDR) to capture users' preferences based on distributed multi-domain data without privacy leakage. PPCDR models both global and local user preferences, characterized by shared and domain-specific tastes, respectively. The main components of PPCDR include a graph transfer module for each domain to fuse global and local user preferences, and a federated update process that applies local differential privacy to collaboratively learn global user preferences. The method is evaluated on three real-world datasets and shows consistent improvements over single- and cross-domain baselines while effectively protecting domain privacy. The key contributions of the paper are the development of a decentralized federated learning framework for CDR and the introduction of PPCDR, which enhances privacy protection and recommendation performance.