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
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
CE-FedGNN enables federated GNN training on coupled distributed graphs via infrequent aggregated representation exchange, moving-average estimation for staleness, and metric-DP, with O(1/sqrt(T)) convergence and O(T^{3/4}) communication.
FedEPD decouples topological purification from semantic recalibration using energy-guided pruning and prototype injection to improve minority performance in federated long-tailed graph learning.
citing papers explorer
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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.
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Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks
CE-FedGNN enables federated GNN training on coupled distributed graphs via infrequent aggregated representation exchange, moving-average estimation for staleness, and metric-DP, with O(1/sqrt(T)) convergence and O(T^{3/4}) communication.
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Towards Federated Long-Tailed Graph Learning: An Energy-Guided Dual Decoupling Approach
FedEPD decouples topological purification from semantic recalibration using energy-guided pruning and prototype injection to improve minority performance in federated long-tailed graph learning.