One-for-All Graph Foundation Model for Federated Graph-Level Clustering

Federated graph-level clustering aims to discover meaningful pattern groups from decentralized graph data. However, most existing methods require training from scratch for each new non-IID setting and cannot rapidly generalize to unseen domains, resulting in substantial computational and communication overhead. To this end, we propose GFM-FGC, a One-for-All Graph Foundation Model for Federated Graph-Level Clustering, following a **pre-train once, adapt locally to each domain, and share knowledge once** paradigm. is first pre-trained to acquire generalizable graph prior knowledge from public graph data. When deployed in a new non-IID setting, the pre-trained model is distributed to clients and efficiently adapted to their private local graphs through lightweight fine-tuning. The locally adapted knowledge is then shared through a single aggregation round to produce a globally coordinated clustering model. In this way, GFM-FGC can rapidly generalize to unseen non-IID settings without repeated end-to-end training or multiple-round client-server communication. Extensive experiments across multiple non-IID settings demonstrate that GFM-FGC achieves competitive clustering performance while providing improved reusability and efficiency over methods trained separately for each scenario.