发表机构
The Hong Kong University of Science and Technology (Guangzhou); Sun Yat-sen University（香港科技大学（广州）; 中山大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出免训练统计估计框架SPEAR，通过拓扑平滑类原型和自适应收缩聚合，解决一次性联邦图学习在极端非独立同分布下的表示错位问题，实现高准确率与数量级加速。
AI 中文摘要
一次性联邦图学习通常旨在通过单轮通信，在具有不连通子图的客户端之间训练图神经网络（GNNs）。现有方法主要在设计先进的优化策略，前提是本地GNN训练不可或缺。然而，实证观察表明，在极端非独立同分布（non-IID）条件下，本地GNN训练遭受严重的跨客户端表示错位，成为错误的主要来源而非补救措施。受此启发，我们将一次性联邦图学习重新表述为统计估计问题。我们提出SPEAR（具有自适应可靠性的统计原型估计），一个完全免训练的框架，直接在原始特征空间中从本地图计算拓扑平滑的类原型。服务器随后使用样本量自适应收缩估计器聚合这些原型，该估计器降低不可靠本地估计的权重，产生稳健的全局类原型。在七个基准上的大量实验表明，SPEAR在极端异质性下始终达到最先进的准确率。此外，SPEAR相比所有基线至少快一个数量级，相对于生成式和蒸馏方法达到数个数量级的加速。我们的发现表明，免训练的统计估计，而非本地GNN优化，为稳健高效的一次性联邦图学习提供了关键。代码可在以下网址获取：此https URL。
英文摘要
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 .
Comments22 pages, 9 figures, including supplementary material
