ParaDa: Pretrained Parameters as Data for Federated Few-Shot Learning

Large pretrained visual models provide a strong foundation for adapting recognition systems to new tasks with limited labeled data. However, in federated few-shot learning, target supervision remains scarce and is further fragmented across clients with heterogeneous observations, making reliable classifier construction difficult. Most existing approaches focus on using these limited target examples more effectively, while overlooking supervision that may already be encoded in the pretrained model itself. We introduce Parameters as Data (**ParaDa**), which treats pretrained classifier parameters as an additional source of supervision for new-task adaptation. For each source class, **ParaDa** pairs its text embedding with the corresponding pretrained classifier weight to train a reusable text-conditioned classifier weight predictor. The predictor produces classifier weights for target classes from their textual descriptions without revisiting source images, while available client examples provide task-specific refinement through their frozen visual features. In the federated setting, this design avoids repeated full-model training and requires only limited communication for classifier refinement. Experiments across diverse visual domains and few-shot protocols show that **ParaDa** achieves competitive recognition with few or no target labels and improves over competing methods on a range of tasks. It also substantially reduces task-specific computation and communication compared with full-model federated adaptation. Beyond the standalone framework, two simple, lightweight extensions use parameter-derived information to substantially enhance federated baselines in data-scarce settings, without complex auxiliary models or additional training stages. These results show that pretrained parameters can serve not only as model components, but also as supervision for data-scarce learning. Code available at [Anonymous GitHub](https://anonymous.4open.science/r/ParaDa-6FBF/README.md).