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

arXiv:2609.31637 · cs.LG, cs.CV · Submitted 2026-09-10 · Read on arXiv

cs.LG, cs.CV

Submitted: 2026-09-10

Updated: 2026-09-10

Code: https://github.com/albarqounilab/fidal

Terminology

• Active Learning — 42× in this paper · explained in 6 episodes
• Accuracy — 23× in this paper · explained in 1 episode
• Benchmark — 16× in this paper · explained in 2 episodes
• Domain Shift — 11× in this paper · explained in 4 episodes
• Federated Learning — 7× in this paper · explained in 14 episodes
• Epistemic Uncertainty — 6× in this paper · explained in 9 episodes
• Aleatoric Uncertainty — 4× in this paper · explained in 4 episodes
• Calibration — 4× in this paper · explained in 7 episodes
• Conference on Computer Vision and Pattern Recognition — 4× in this paper
• Embeddings — 4× in this paper · explained in 2 episodes
• Foundation Model — 4× in this paper · explained in 3 episodes
• Non-IID Data — 4× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Aggregation — 3× in this paper · explained in 1 episode
• Linear probe — 3× in this paper · explained in 2 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Core — 2× in this paper · explained in 2 episodes
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Distribution Shift — 2× in this paper · explained in 5 episodes
• Data Heterogeneity — 1× in this paper · explained in 3 episodes
• Federated Learning (FL) — 1× in this paper · explained in 7 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Active Federated Learning
• Deep Active Learning with Contrastive Learning Under Realistic Data Pool Assumptions
• SAFER: A Calibrated Risk-Aware Multimodal Recommendation Model for Dynamic Treatment Regimes
• Generative Adversarial Active Learning

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