A federated graph neural network framework for privacy-preserving personalization.

ABSTRACT: 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. Expe

SUBMITTER: Wu C 

PROVIDER: S-EPMC9163103 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature