Heterogeneity-Aware Knowledge Sharing for Graph Federated Learning

Abstract

Graph Federated Learning (GFL) enables distributed graph representation learning while preserving graph data privacy. However, it suffers from heterogeneity in node features and graph structures across clients. To address this challenge, we propose a novel graph Federated learning method via Semantic and Structural Alignment (FedSSA). For node feature heterogeneity, FedSSA infers class-wise node distributions through a variational model, clusters clients according to the inferred distributions, and aligns local distributions with cluster-level representatives. For structural heterogeneity, FedSSA employs spectral Graph Neural Networks (GNNs) and introduces a spectral energy measure to characterize graph topology, enabling structural alignment between local and cluster-level spectral GNNs. Experiments on eleven homophilic and heterophilic graph datasets under non-overlapping and overlapping partitioning settings demonstrate that FedSSA consistently outperforms eleven state-of-the-art methods. Our code is available at https://github.com/blgpb/FedSSA.