FedGOG: Federated Graph Out-of-Distribution Generalization with Diffusion Data Exploration and Latent Embedding Decorrelation

Abstract

Federated graph learning (FGL) has emerged as a promising approach to enable collaborative training of graph models while preserving data privacy. However, current FGL methods overlook the out-of-distribution (OOD) shifts that occur in real-world scenarios. The distribution shifts between training and testing datasets in each client impact the FGL performance. To address this issue, we propose federated graph OOD generalization framework FedGOG, which includes two modules, i.e., diffusion data exploration (DDE) and latent embedding decorrelation (LED). In DDE, all clients jointly train score models to accurately estimate the global graph data distribution and sufficiently explore sample space using score-based graph diffusion with conditional generation. In LED, each client models a global invariant GNN and a personalized spurious GNN. LED aims to decorrelate spuriousness from invariant relationships by minimizing the mutual information between two categories of latent embeddings from different GNN models. Extensive experiments on six benchmark datasets demonstrate the superiority of FedGOG.