Federated Graph Semantic and Structural Learning

TL;DR

This paper introduces a federated graph learning method that addresses local distortion caused by semantics and structure, enhancing discrimination and structural preservation across distributed graph data.

Contribution

It reveals the causes of local distortion in federated graph learning and proposes a novel approach to align semantics and structure for improved performance.

Findings

Outperforms existing methods on three graph datasets

Effectively preserves structural information and discriminability

Addresses node-level and graph-level distortion issues

Abstract

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.