Federated Few-Shot Graph Domain Generalization with Non-Aligned Label Spaces

Federated domain generalization (FDG) on graphs aims to collaboratively learn from decentralized source domains and generalize to unseen target domains without centralizing private graph data. Existing methods typically assume a predefined prediction task with globally aligned label spaces, limiting their applicability to heterogeneous graph domains with different class cardinalities and semantics. To address this limitation, we formulate **Federated Few-Shot Graph Domain Generalization with Non-Aligned Label Spaces**, where clients learn transferable model parameters without cross-domain label correspondence and predict query nodes in an unseen graph from only a few labeled context nodes. We further propose **FedDARC**, which first characterizes graph domains through the optimization responses of a common reference encoder, learns geometry-preserving domain states, and uses them to residually calibrate node representations. It then performs label-space-agnostic relational inference by constructing task-relative labels and aggregating query-to-context relational evidence, avoiding globally shared class semantics or a fixed-output classifier. At inference, the learned shared model remain fixed, while the target domain state and task-relative labels are derived from the labeled context nodes for prediction. Extensive experiments on graph datasets demonstrate the effectiveness of FedDARC in generalizing to unseen graph domains with non-aligned label spaces. Our code is available at https://anonymous.4open.science/r/fedgraph-0B15.