Federated Learning (FL) has emerged as a promising approach for preserving data privacy in recommendation systems by training models locally. Recently, Graph Neural Networks (GNN) have gained popularity in recommendation tasks due to their ability to capture high-order interactions between users and items. However, privacy concerns prevent the global sharing of the entire user-item graph. To address this limitation, some methods create pseudo-interacted items or users in the graph to compensate for missing information for each client. Unfortunately, these methods introduce random noise and raise privacy concerns. In this paper, we propose FedRKG, a novel federated recommendation system, where a global knowledge graph (KG) is constructed and maintained on the server using publicly available item information, enabling higher-order user-item interactions. On the client side, a relation-aware GNN model leverages diverse KG relationships. To protect local interaction items and obscure gradients, we employ pseudo-labeling and Local Differential Privacy (LDP). Extensive experiments conducted on three real-world datasets demonstrate the competitive performance of our approach compared to centralized algorithms while ensuring privacy preservation. Moreover, FedRKG achieves an average accuracy improvement of 4% compared to existing federated learning baselines.

翻译：联邦学习通过在本地训练模型，已成为推荐系统中保护数据隐私的一种有前景的方法。近年来，图神经网络因其捕捉用户与物品间高阶交互的能力，在推荐任务中日益普及。然而，隐私问题阻碍了全局用户-物品图的共享。为克服这一限制，部分方法在图中创建伪交互物品或用户，以补偿各客户端缺失的信息。遗憾的是，这些方法会引入随机噪声并引发隐私担忧。本文提出FedRKG——一种新型联邦推荐系统，其中利用公开可用的物品信息在服务器端构建并维护全局知识图谱，从而实现高阶用户-物品交互。在客户端侧，关系感知的图神经网络模型利用了知识图谱的多样关系。为保护本地交互物品并模糊梯度，我们采用伪标签和本地差分隐私技术。在三个真实数据集上的大量实验表明，本方法在确保隐私保护的同时，实现了与集中式算法相媲美的竞争性能。此外，与现有联邦学习基线相比，FedRKG的平均准确率提升达4%。