FedGMC introduces dual manifold calibration to balance global commonalities and local personalization in graph federated learning, outperforming rigid alignment baselines on eleven homophilic and heterophilic graphs.
FedGNN: Federated graph neural network for privacy-preserving recommendation
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Beyond Rigid Alignment: Graph Federated Learning via Dual Manifold Calibration
FedGMC introduces dual manifold calibration to balance global commonalities and local personalization in graph federated learning, outperforming rigid alignment baselines on eleven homophilic and heterophilic graphs.