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.

翻译：图神经网络（GNN）因其能够捕捉用户与物品节点间的高阶结构信息，已在推荐系统中获得广泛青睐。然而，这些方法需要收集用户与对应物品之间的个人交互数据，并在中央服务器上建模，这将违反GDPR等隐私法规。迄今为止，尚无现有工作能在不泄露每个用户私有交互数据（即其子图）的情况下构建全局图。本文首次设计了一种基于GNN的新型无损联邦推荐框架，该框架能够以完整的高阶结构信息实现全图训练，使训练过程与对应的非联邦方法等价。此外，我们使用LightGCN实例化该框架的一个示例，并证明了其等效性。