  Recently, Federated Graph Learning (FGL) has attracted significant attention
as a distributed framework based on graph neural networks, primarily due to its
capability to break data silos. Existing FGL studies employ community split on
the homophilous global graph by default to simulate federated semi-supervised
node classification settings. Such a strategy assumes the consistency of
topology between the multi-client subgraphs and the global graph, where
connected nodes are highly likely to possess similar feature distributions and
the same label. However, in real-world implementations, the varying
perspectives of local data engineering result in various subgraph topologies,
posing unique heterogeneity challenges in FGL. Unlike the well-known label
Non-independent identical distribution (Non-iid) problems in federated
learning, FGL heterogeneity essentially reveals the topological divergence
among multiple clients, namely homophily or heterophily. To simulate and handle
this unique challenge, we introduce the concept of structure Non-iid split and
then present a new paradigm called \underline{Ada}ptive \underline{F}ederated
\underline{G}raph \underline{L}earning (AdaFGL), a decoupled two-step
personalized approach. To begin with, AdaFGL employs standard multi-client
federated collaborative training to acquire the federated knowledge extractor
by aggregating uploaded models in the final round at the server. Then, each
client conducts personalized training based on the local subgraph and the
federated knowledge extractor. Extensive experiments on the 12 graph benchmark
datasets validate the superior performance of AdaFGL over state-of-the-art
baselines. Specifically, in terms of test accuracy, our proposed AdaFGL
outperforms baselines by significant margins of 3.24\% and 5.57\% on community
split and structure Non-iid split, respectively.
