  Subgraphs of a larger global graph may be distributed across multiple
devices, and only locally accessible due to privacy restrictions, although
there may be links between subgraphs. Recently proposed subgraph Federated
Learning (FL) methods deal with those missing links across local subgraphs
while distributively training Graph Neural Networks (GNNs) on them. However,
they have overlooked the inevitable heterogeneity between subgraphs comprising
different communities of a global graph, consequently collapsing the
incompatible knowledge from local GNN models. To this end, we introduce a new
subgraph FL problem, personalized subgraph FL, which focuses on the joint
improvement of the interrelated local GNNs rather than learning a single global
model, and propose a novel framework, FEDerated Personalized sUBgraph learning
(FED-PUB), to tackle it. Since the server cannot access the subgraph in each
client, FED-PUB utilizes functional embeddings of the local GNNs using random
graphs as inputs to compute similarities between them, and use the similarities
to perform weighted averaging for server-side aggregation. Further, it learns a
personalized sparse mask at each client to select and update only the
subgraph-relevant subset of the aggregated parameters. We validate our FED-PUB
for its subgraph FL performance on six datasets, considering both
non-overlapping and overlapping subgraphs, on which it significantly
outperforms relevant baselines. Our code is available at
https://github.com/JinheonBaek/FED-PUB.
