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Rethinking One-Shot Federated Graph Learning: Training-Free Statistical Estimation
One-shot federated graph learning generally aims to train Graph Neural Networks (GNNs) across clients with disconnected subgraphs in a single communication round. Existing methods predominantly design advanced optimization strategies under the premise that local GNN training is indispensable. Howeve
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