Dingyi Zhao

FedSST: Rethinking Fair Federated Graph Learning under Structural Shift

Federated Graph Learning (FGL) offers a privacy-preserving paradigm for collaborative training on graph data, yet significant topological heterogeneity poses a critical threat to generalization fairness, often yielding a global model dominated by a subset of clients. This introduces two critical iss