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.