Rethinking One-Shot Federated Graph Learning: Training-Free Statistical Estimation

• URL: http://arxiv.org/abs/2609.06154v1
• Date: Sat, 05 Sep 2026 15:55:22 GMT
• Title: Rethinking One-Shot Federated Graph Learning: Training-Free Statistical Estimation
• Abstract summary: One-shot federated graph learning generally aims to train Graph Neural Networks (GNNs) across clients with disconnected subgraphs in a single communication round.<n>Under extreme non-IID conditions, local GNN training suffers from severe cross-client representation misalignment.<n>We propose SPEAR, a training-free framework that directly computes topology-smoothed class prototypes from local graphs.
• Score: 12.66442044096499
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. However, empirical observations reveal that under extreme non-IID conditions, local GNN training suffers from severe cross-client representation misalignment, becoming a major source of error rather than a remedy. Motivated by this, we reformulate one-shot FGL as a statistical estimation problem. We propose SPEAR (Statistical Prototype Estimation with Adaptive Reliability), a completely training-free framework that directly computes topology-smoothed class prototypes from local graphs in the original feature space. The server then aggregates these prototypes using a sample-size-adaptive shrinkage estimator that down-weights unreliable local estimates, producing robust global class prototypes. Extensive experiments across seven benchmarks demonstrate that SPEAR consistently achieves state-of-the-art accuracy under extreme heterogeneity. Moreover, SPEAR delivers at least an order-of-magnitude speedup over all baselines, reaching several orders of magnitude against generative and distillation-based methods. Our findings suggest that training-free statistical estimation, rather than local GNN optimization, provides the key to robust and efficient one-shot federated graph learning. The code is available at https://github.com/Yodeesy/SPEAR .

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