Author

D. Gaviria

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#federated learning Open access Oct 2026

Reducing Clinician Annotation Fatigue in Open-Set Federated Learning: A Client-Adaptive Vision-Language Gatekeeper.

PURPOSE Federated learning enables breast-imaging sites to jointly train mammography artificial intelligence (AI) without sharing images, but radiologists at each site must still annotate selected images during active-learning rounds. We developed and evaluated a client-adaptive vision-language gatekeeper that withhold...

Adea Nesturi, D. Gaviria, Jia-Jun Zeng et al. · Journal of the American Coll... · 0 citations

#machine learning Preprint Sep 2026

FIDAL: Diversity-Aware Federated Active Learning Under Real-World Distribution Shifts

Federated learning enables collaborative model training across institutions without centralizing data, yet high annotation costs, domain shifts, and class imbalance remain major obstacles, especially when irrelevant out-of-distribution (OOD) samples dilute the labeled data. Existing active learning methods target uncer...

D. Gaviria, Shadi Albarqouni · 0 citations