  Federated graph learning collaboratively learns a global graph neural network
with distributed graphs, where the non-independent and identically distributed
property is one of the major challenges. Most relative arts focus on
traditional distributed tasks like images and voices, incapable of graph
structures. This paper firstly reveals that local client distortion is brought
by both node-level semantics and graph-level structure. First, for node-level
semantics, we find that contrasting nodes from distinct classes is beneficial
to provide a well-performing discrimination. We pull the local node towards the
global node of the same class and push it away from the global node of
different classes. Second, we postulate that a well-structural graph neural
network possesses similarity for neighbors due to the inherent adjacency
relationships. However, aligning each node with adjacent nodes hinders
discrimination due to the potential class inconsistency. We transform the
adjacency relationships into the similarity distribution and leverage the
global model to distill the relation knowledge into the local model, which
preserves the structural information and discriminability of the local model.
Empirical results on three graph datasets manifest the superiority of the
proposed method over its counterparts.
