Conventional recommender systems are required to train the recommendation model using a centralized database. However, due to data privacy concerns, this is often impractical when multi-parties are involved in recommender system training. Federated learning appears as an excellent solution to the data isolation and privacy problem. Recently, Graph neural network (GNN) is becoming a promising approach for federated recommender systems. However, a key challenge is to conduct embedding propagation while preserving the privacy of the graph structure. Few studies have been conducted on the federated GNN-based recommender system. Our study proposes the first vertical federated GNN-based recommender system, called VerFedGNN. We design a framework to transmit: (i) the summation of neighbor embeddings using random projection, and (ii) gradients of public parameter perturbed by ternary quantization mechanism. Empirical studies show that VerFedGNN has competitive prediction accuracy with existing privacy preserving GNN frameworks while enhanced privacy protection for users' interaction information.

翻译：传统推荐系统需使用集中式数据库训练推荐模型，然而当多方参与推荐系统训练时，由于数据隐私问题，这一方法通常难以实现。联邦学习成为解决数据孤岛与隐私问题的优秀方案。近年来，图神经网络正逐渐成为联邦推荐系统领域极具前景的方法，但如何在保护图结构隐私的同时进行嵌入传播仍是一大关键挑战。目前针对基于联邦图神经网络的推荐系统研究较少。本研究首次提出基于纵向联邦图神经网络的推荐系统VerFedGNN，我们设计了一个框架用于传输：（i）通过随机投影实现的邻居嵌入求和结果，以及（ii）经三元量化机制扰动的公共参数梯度。实证研究表明，VerFedGNN在增强用户交互信息隐私保护的同时，其预测精度与现有隐私保护图神经网络框架具有竞争力。