FOCUS:UNKNOWN-AWARE PROMPTING AND SE- MANTIC INJECTION FOR OPEN-SET DOMAIN GENER- ALIZATION IN FEDERATED SETTINGS

We introduce Federated Open-Set Domain Generalization (FODG), a novel paradigm that requires decentralized models to reconcile domain shifts while identifying novel categories. While prompt-based federated learning has advanced domain generalization, existing methods predominantly operate under a closed-set assumption and falter in data-scarce regimes, leading to catastrophic misclassification of unknown samples. To address these challenges, we propose FOCUS (Federated Open-Set Cross-Domain Unknown Separation), an innovative federated prompt-learning framework with two core breakthroughs. Specifically, FOCUS reformulates the task as a unified $|\mathcal{C}|+1$ prompt learning problem by introducing an additional unknown-aware prompt, enabling federated vision-language models to learn transferable decision boundaries between known and unseen categories across decentralized domains. Furthermore, to combat severe domain shifts in data-scarce federated settings, we develop a cross-attention-based attribute-semantic injection mechanism that dynamically grounds textual attributes into instance-specific visual features. By synergizing this mechanism with a hierarchical multi-modal prompt learner and a class-diversity regularization that enforces orthogonal semantic representations, our framework facilitates robust domain-invariant adaptation and strictly prevents feature entanglement between known and novel categories. Extensive experiments on five benchmark datasets demonstrate that FOCUS consistently outperforms state-of-the-art methods under challenging few-shot federated settings. Code is available at https://anonymous.4open.science/r/FOCUS-2104.