Graphs have been widely used in data mining and machine learning due to their unique representation of real-world objects and their interactions. As graphs are getting bigger and bigger nowadays, it is common to see their subgraphs separately collected and stored in multiple local systems. Therefore, it is natural to consider the subgraph federated learning setting, where each local system holding a small subgraph that may be biased from the distribution of the whole graph. Hence, the subgraph federated learning aims to collaboratively train a powerful and generalizable graph mining model without directly sharing their graph data. In this work, towards the novel yet realistic setting of subgraph federated learning, we propose two major techniques: (1) FedSage, which trains a GraphSage model based on FedAvg to integrate node features, link structures, and task labels on multiple local subgraphs; (2) FedSage+, which trains a missing neighbor generator along FedSage to deal with missing links across local subgraphs. Empirical results on four real-world graph datasets with synthesized subgraph federated learning settings demonstrate the effectiveness and efficiency of our proposed techniques. At the same time, consistent theoretical implications are made towards their generalization ability on the global graphs.

本文对子图联邦学习问题提出两种主要技术，即基于FedAvg的FedSage模型和带有缺失邻居生成器的FedSage +模型，以协作地训练功能强大且具有泛化能力的图形挖掘模型而无需直接共享图形数据。实验结果表明我们所提出的方法在四个真实世界的图形数据集中，表现出了良好的效果和效率，并且在理论上证明了其对于全局图的泛化能力。

子图联邦学习与缺失邻居生成