Currently, the federated graph neural network (GNN) has attracted a lot of attention due to its wide applications in reality without violating the privacy regulations. Among all the privacy-preserving technologies, the differential privacy (DP) is the most promising one due to its effectiveness and light computational overhead. However, the DP-based federated GNN has not been well investigated, especially in the sub-graph-level setting, such as the scenario of recommendation system. The biggest challenge is how to guarantee the privacy and solve the non independent and identically distributed (non-IID) data in federated GNN simultaneously. In this paper, we propose DP-FedRec, a DP-based federated GNN to fill the gap. Private Set Intersection (PSI) is leveraged to extend the local graph for each client, and thus solve the non-IID problem. Most importantly, DP is applied not only on the weights but also on the edges of the intersection graph from PSI to fully protect the privacy of clients. The evaluation demonstrates DP-FedRec achieves better performance with the graph extension and DP only introduces little computations overhead.

翻译：目前,联邦制图形神经网络(GNN)由于在不违反隐私条例的情况下在现实中的广泛应用而引起人们的极大关注。在所有隐私保护技术中,差异隐私(DP)因其有效性和轻计算间接费用而是最有希望的(DP),然而,没有很好地调查基于DP的联邦式GNN(GNN),特别是在子图级设置中,例如建议系统的设想。最大的挑战是如何保证隐私并解决在联邦制GNN中不独立和同样分布的(非IID)数据。在本文中,我们提议DP-FedRec(DP-FedRec),即基于DP-FedRec(FedRec)的联邦式GNN(DP-FedRec),以填补这一空白。私人Set Intercrection(PSI)利用来扩展每个客户的本地图,从而解决非IID问题。最重要的是,DP不仅适用于重量,而且还适用于PSI交叉图的边缘,以充分保护客户隐私。评价显示DP-FedRec(FedRec)取得更好的业绩,而DP只是少量计算间接费用。