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：传统推荐系统需要使用集中式数据库来训练推荐模型。然而，由于数据隐私问题，当多方参与推荐系统训练时，这种方法通常不切实际。联邦学习为解决数据隔离和隐私问题提供了一个优秀的解决方案。近年来，图神经网络（GNN）正成为联邦推荐系统的一种有前景的方法。然而，一个关键的挑战是在保护图结构隐私的同时进行嵌入传播。目前基于联邦GNN的推荐系统研究较少。本研究提出了首个基于垂直联邦GNN的推荐系统，称为VerFedGNN。我们设计了一个框架来传输：（i）使用随机投影的邻居嵌入求和，以及（ii）通过三元量化机制扰动的公共参数梯度。实证研究表明，VerFedGNN在预测准确性上与现有的隐私保护GNN框架具有竞争力，同时增强了对用户交互信息的隐私保护。