PFGRS: A Privacy-preserving Subgraph-level Federated Graph learning for Recommender System

Abstract

The federated graph recommender system has garnered significant attention due to its broad applicability and the capabilities of Graph Neural Networks. Addressing the challenges of non-independent and identically distributed (Non-IID) data, along with ensuring privacy while achieving nodes’ features sharing among clients, is pivotal in federated graph recommender systems. In this study, we introduce the Deep neural network-based Graph Convolutional Collaborative Filtering model (DGCF), comprising two key modules: the DEEP module and the GCN module. The DEEP module, a feedforward neural network, can learn high-order feature interactions within users and items, while the GCN module can capture collaborative signals. Building upon DGCF, we propose a Privacy-preserving Subgraph-level Federated Graph Learning for Recommender System (PFGRS). To mitigate the Non-IID problem and extend the local graph for each client, PFGRS leverages differential privacy and trusted execution environments, which avoids the introduction of third-party servers and local differential privacy. More importantly, by segregating local training into independent training and extended training, PFGRS enables nodes’ feature to be shared indirectly among clients in federated learning. We conduct comprehensive experiments on real datasets. The experimental results not only show the superior performance of DGCF but also well demonstrate the significant effectiveness of PFGRS.