NOMAD: Federated Generalized Category Discovery Across Unseen Domains

Generalized Category Discovery (GCD) classifies known categories while discovering novel ones from unlabeled data. Practical deployments add two constraints rarely studied together: source data are distributed across institutions that cannot share raw samples, and the model must generalize to an unseen domain. Federated GCD rarely evaluates held-out-domain transfer, and domain-generalized GCD assumes centralized data. We formalize their intersection as **Federated Domain Generalization for Generalized Category Discovery (FedDG-GCD)**: decentralized clients train a model that must recognize known and discover novel categories in an unseen target domain without target-domain access. To our knowledge, this setting is unexplored, and no evaluation protocol exists for it. We propose **NOMAD** (**N**on-IID **O**pen-world **M**odel for **A**daptive **D**iscovery), combining a raw-data-free *Global Style Bank* for cross-client appearance diversification, *Adaptive Prototypical Alignment* for stable known-class anchors and confusable-prototype separation, and *perturbation-stability aggregation* to down-weight updates with unstable saliency under Style-Bank perturbations. A conditional target-risk decomposition relates these components to federated estimation error, residual style discrepancy, client heterogeneity, prototype error, and open-world routing and clustering. On PACS, Office-Home, and Mini-DomainNet under Dirichlet heterogeneity ($\beta \in 0.2,0.05$), **NOMAD** improves overall accuracy by **+2.5 to +4.0 points** over the strongest compared method in each setting , while remaining competitive on both old and novel classes.