Coordination on a Budget: Federated Active Learning with Few Labels

arXiv:2608.18634 · cs.LG · Submitted 2026-08-19 · Read on arXiv

cs.LG

Submitted: 2026-08-19

Updated: 2026-09-07

Comments: 16 pages

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Active Learning — 28× in this paper · explained in 6 episodes
• Redundancy — 23× in this paper · explained in 2 episodes
• Federated Learning — 18× in this paper · explained in 15 episodes
• Federated Active Learning — 15× in this paper
• Embeddings — 14× in this paper · explained in 2 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Contrastive Learning — 7× in this paper · explained in 5 episodes
• Non-IID Data — 7× in this paper · explained in 2 episodes
• Representation Learning — 7× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Adaptation — 5× in this paper · explained in 1 episode
• Data Heterogeneity — 5× in this paper · explained in 3 episodes
• Aggregation — 4× in this paper · explained in 1 episode
• Differential Privacy — 4× in this paper · explained in 4 episodes
• Federated Learning (FL) — 4× in this paper · explained in 7 episodes
• AUC — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Active Learning Strategy — 1× in this paper · explained in 1 episode
• Affective Computing — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes

Sources

• Generalized Coverage for More Robust Low-Budget Active Learning
• Active Federated Learning

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks