  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 holds 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.
