GNN4FR: A Lossless GNN-based Federated Recommendation Framework

GNNs are widely used in personalized recommendation methods as they are able to capture high-order interactions between users and items in a user-item graph, enhancing user and item representations [2, 4, 15, 16, 19, 20]. However, these methods face challenges in terms of privacy laws, such as GDPR [14] as they require the collection and modeling of personal data in a central server. Constructing the global graph using all users' subgraphs is often not allowed. Therefore, existing works [12, 17] just expand a user's local graph to exploit high-order information. In this paper, we propose the first lossless federated framework named GNN4FR, which can accommodate almost all existing graph neural networks (GNNs) based recommenders. The contributions of this paper are summarized as follows: We propose a novel lossless federated framework for GNN-based methods, which enables the training process to be equivalent to the corresponding un-federated counterpart. We propose an "expanding local subgraph + synchronizing user embedding" mechanism to achieve full-graph training.