Distribution-Calibrated Diffusion for One-Shot Federated Graph Classification

Federated learning enables multiple clients to collaboratively train models without sharing their private data. However, conventional approaches typically require repeated client–server communication, leading to substantial communication overhead and repeated exposure of client model parameters. One-shot federated learning has therefore attracted increasing attention by restricting client–server interaction to a single communication round. Recently, this paradigm has been extended from image data to graph data, giving rise to one-shot federated graph learning. However, existing one-shot federated graph learning studies mainly focus on node-level tasks, while one-shot learning for graph-level classification remains largely unexplored. Under strict one-shot communication, the server must reconstruct reliable graph distributions and reconcile heterogeneous client data without any subsequent client feedback. To bridge this gap, we propose a one-shot federated graph classification framework that leverages diffusion models to transfer and reconstruct client graph distributions. Specifically, clients upload diffusion-based generative knowledge and compact graph distribution summaries only once, after which the server generates, validates, and reweights synthetic graphs to reconstruct and fuse client graph distributions. Extensive experiments demonstrate that our framework consistently outperforms state-of-the-art FGL and one-shot FGL baselines.