Personalized Graph Transformer for Federated Graph Learning

Abstract

Federated graph learning (FGL) empowers distributed training of subgraphs across multiple institutions, overcoming the challenges of data silos and inter-institutional data sharing. Existing federated subgraph methods achieve collaborative training by training local models on clients and uploading them to a server for parameter aggregation. However, most existing methods do not take into account for the heterogeneity of client subgraph data, leading to suboptimal results. To deal with this challenging problem, we propose a personalized FGL method named FedPGT. FedPGT employs a single-layer transformer to capture long-term dependencies between client nodes, and pools client subgraphs into an averaged node representation for subgraph similarity calculation. We validate the effectiveness of FedPGT through extensive experiments on two subgraph scenarios and five datasets. The results demonstrate that FedPGT significantly outperforms the baseline methods and mitigates the adverse impact of heterogeneity.