E²Gen: Evidential Energy-Based Generation for Fair Federated Graph Learning

Federated Graph Learning is rapidly evolving as a privacy-preserving collaborative approach for decentralized graph data. However, severe fairness challenges are increasingly undermining federated systems by systematically degrading predictions for structurally disadvantaged minority nodes. The inherent vulnerabilities and missing topological contexts in Federated Graph Learning are deeply entangled, making traditional federated fairness methods and simple oversampling less effective. In our work, we propose an effective Evidential Energy-based Generation framework for Fair Federated Graph Learning ($E^2Gen$). At the client level, it explicitly identifies structurally deficient nodes using a multi-axis metric, synthesizing targeted representations via conditional energy-based models, and selects reliable samples through an evidential quality gate. At the server level, the local performance disparities uploaded by each client are evaluated to construct a fairness gap assessment, making the global model absorb equitable improvements by further adjusting the aggregation weights. Our method can handle high topological heterogeneity, does not require strict generative normalization, and is effective under both homophilic and heterophilic graph structures. Extensive results on various settings of federated graph scenarios under severe fairness challenges validate the effectiveness of this approach. The code is anonymously available at https://anonymous.4open.science/r/E-Gen-A2C6.