Beyond Rigid Alignment: Graph Federated Learning via Dual Manifold Calibration

ArXi:2605.06260v1 Announce Type: new Graph Federated Learning (GFL) enables collaborative representation learning across distributed subgraphs while preserving privacy. However, heterogeneity remains a critical challenge, as subgraphs across clients typically differ significantly in both semantics and structures. Existing methods address heterogeneity by enforcing the rigid alignment of model parameters or prototypes between clients and the server.