Graph Neural Networks (GNNs) training often necessitates gathering raw user data on a central server, which raises significant privacy concerns. Federated learning emerges as a solution, enabling collaborative model training without users directly sharing their raw data. However, integrating federated learning with GNNs presents unique challenges, especially when a client represents a graph node and holds merely a single feature vector. In this paper, we propose a novel framework for node-level federated graph learning. Specifically, we decouple the message-passing and feature vector transformation processes of the first GNN layer, allowing them to be executed separately on the user devices and the cloud server. Moreover, we introduce a graph Laplacian term based on the feature vector's latent representation to regulate the user-side model updates. The experiment results on multiple datasets show that our approach achieves better performance compared with baselines.

该研究解决了图神经网络训练中的隐私问题，即需将用户数据集中到服务器。提出了一种新颖的节点级联邦学习框架，解耦了第一层GNN的消息传递和特征向量转换过程，使其可在用户设备和云服务器上分别执行。实验结果表明，该方法在多个数据集上相较基线具有更好的性能。

每个用户一个节点：图神经网络的节点级联邦学习