A federated graph neural network framework for privacy-preserving personalization

• Published in: Nature communications Vol. 13; no. 1; p. 3091
• Main Authors: Wu, Chuhan, Wu, Fangzhao, Lyu, Lingjuan, Qi, Tao, Huang, Yongfeng, Xie, Xing
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02.06.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 706/689/179; 706/703/166; 706/703/66; Customization; Graph neural networks; Graphs; Humanities and Social Sciences; Learning; multidisciplinary; Neural networks; Privacy; Recommender systems; Science; Science (multidisciplinary)
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-30714-9

Graph neural network (GNN) is effective in modeling high-order interactions and has been widely used in various personalized applications such as recommendation. However, mainstream personalization methods rely on centralized GNN learning on global graphs, which have considerable privacy risks due to the privacy-sensitive nature of user data. Here, we present a federated GNN framework named FedPerGNN for both effective and privacy-preserving personalization. Through a privacy-preserving model update method, we can collaboratively train GNN models based on decentralized graphs inferred from local data. To further exploit graph information beyond local interactions, we introduce a privacy-preserving graph expansion protocol to incorporate high-order information under privacy protection. Experimental results on six datasets for personalization in different scenarios show that FedPerGNN achieves 4.0% ~ 9.6% lower errors than the state-of-the-art federated personalization methods under good privacy protection. FedPerGNN provides a promising direction to mining decentralized graph data in a privacy-preserving manner for responsible and intelligent personalization. Mainstream personalization methods rely on centralized Graph Neural Network learning on global graphs, which have considerable privacy risks due to the privacy-sensitive nature of user data. Here, the authors present a federated GNN framework for both effective and privacy-preserving personalization.