Personal interaction data can be effectively modeled as individual graphs for each user in recommender systems.Graph Neural Networks (GNNs)-based recommendation techniques have become extremely popular since they can capture high-order collaborative signals between users and items by aggregating the individual graph into a global interactive graph.However, this centralized approach inherently poses a threat to user privacy and security. Recently, federated GNN-based recommendation techniques have emerged as a promising solution to mitigate privacy concerns. Nevertheless, current implementations either limit on-device training to an unaccompanied individual graphs or necessitate reliance on an extra third-party server to touch other individual graphs, which also increases the risk of privacy leakage. To address this challenge, we propose a Cluster-enhanced Federated Graph Neural Network framework for Recommendation, named CFedGR, which introduces high-order collaborative signals to augment individual graphs in a privacy preserving manner. Specifically, the server clusters the pretrained user representations to identify high-order collaborative signals. In addition, two efficient strategies are devised to reduce communication between devices and the server. Extensive experiments on three benchmark datasets validate the effectiveness of our proposed methods.

翻译：在推荐系统中，个人交互数据可有效建模为每个用户的独立图。基于图神经网络（GNNs）的推荐技术因能将独立图聚合为全局交互图以捕获用户与物品间的高阶协同信号而广受欢迎。然而，这种中心化方法本质上对用户隐私与安全构成威胁。近年来，基于联邦学习的图神经网络推荐技术已成为缓解隐私问题的可行方案。但现有方案要么仅支持在未关联的独立图上进行设备端训练，要么需依赖额外第三方服务器接触其他独立图，这同样增加了隐私泄露风险。为应对这一挑战，我们提出一种基于聚类的增强型联邦图神经网络推荐框架（命名为CFedGR），该框架以隐私保护方式引入高阶协同信号来增强独立图。具体而言，服务器通过对预训练用户表征进行聚类来识别高阶协同信号。此外，本文设计了两种高效策略以减少设备与服务器间的通信开销。在三个基准数据集上的大量实验验证了所提方法的有效性。