Author

Shu-Tong Zheng

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Preprint Sep 2026

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

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, suggests that training-free statistical estimation, rather than local GNN optimization, provides the key to...

Shu-Tong Zheng, Si-An Chen · 0 citations

#machine learning Preprint Sep 2026

Structural Entropy-Driven Graph Diffusion Generation for One-Shot Federated Graph Learning

SPIRE, a Structural Entropy-Driven Graph Diffusion Generation method that introduces topology-aware client differentiation into one-shot FGL, employs first-order degree-distribution structural entropy as a compact descriptor of degree-mass dispersion and uses it to derive structural client weights, providing an inducti...

Shu-Tong Zheng, Le-Le Fu, Sheng Huang et al. · 0 citations

#machine learning Preprint Sep 2026

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. However, empirical observa...

Shu-Tong Zheng, Si-An Chen · 0 citations