GNN4FR: A Lossless GNN-based Federated Recommendation Framework

• URL: http://arxiv.org/abs/2308.01197v1
• Date: Tue, 25 Jul 2023 16:55:17 GMT
• Title: GNN4FR: A Lossless GNN-based Federated Recommendation Framework
• Authors: Guowei Wu and Weike Pan and Zhong Ming
• Abstract summary: Graph neural networks (GNNs) have gained wide popularity in recommender systems. Our framework achieves full-graph training with complete high-order structure information. In addition, we use LightGCN to instantiate an example of our framework and show its equivalence.
• Score: 13.672867761388675
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph neural networks (GNNs) have gained wide popularity in recommender systems due to their capability to capture higher-order structure information among the nodes of users and items. However, these methods need to collect personal interaction data between a user and the corresponding items and then model them in a central server, which would break the privacy laws such as GDPR. So far, no existing work can construct a global graph without leaking each user's private interaction data (i.e., his or her subgraph). In this paper, we are the first to design a novel lossless federated recommendation framework based on GNN, which achieves full-graph training with complete high-order structure information, enabling the training process to be equivalent to the corresponding un-federated counterpart. In addition, we use LightGCN to instantiate an example of our framework and show its equivalence.

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