FIDAL: Diversity-Aware Federated Active Learning Under Real-World Distribution Shifts
arXiv:2609.31637v1 Announce Type: new Abstract: 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 d…
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- 2026-09-29 04:00 · arXiv cs.LG
FIDAL: Diversity-Aware Federated Active Learning Under Real-World Distribution Shifts