Shunxin Xiao

Homophily-Heterogeneity Gradient Surgery for Federated Graph Learning

Federated Graph Learning (FGL) facilitates privacy-preserving collaborative training of graph neural networks, yet homophily heterogeneity across subgraphs triggers optimization conflicts that degrade model generalization. Most existing solutions rely on multi-channel architectures to mitigate such …