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Graphs have been widely used in data mining and machine learning due to theirunique representation of real-world objects and their interactions. As graphsare getting bigger and bigger nowadays, it is common to see their subgraphsseparately collected and stored in multiple local systems. Therefore, it isnatural to consider the subgraph federated learning setting, where each localsystem holds a small subgraph that may be biased from the distribution of thewhole graph. Hence, the subgraph federated learning aims to collaborativelytrain a powerful and generalizable graph mining model without directly sharingtheir graph data. In this work, towards the novel yet realistic setting ofsubgraph federated learning, we propose two major techniques: (1) FedSage,which trains a GraphSage model based on FedAvg to integrate node features, linkstructures, and task labels on multiple local subgraphs; (2) FedSage+, whichtrains a missing neighbor generator along FedSage to deal with missing linksacross local subgraphs. Empirical results on four real-world graph datasetswith synthesized subgraph federated learning settings demonstrate theeffectiveness and efficiency of our proposed techniques. At the same time,consistent theoretical implications are made towards their generalizationability on the global graphs.

Subgraph Federated Learning with Missing Neighbor Generation