mlsanityData to 7 Oct 2026

Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning

arXiv 2202.00195 ↗First posted 1 Feb 2022cs.LGcited by 13

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Abstract (arXiv)

Federated learning (FL) has been intensively investigated in terms of communication efficiency, privacy, and fairness. However, efficient annotation, which is a pain point in real-world FL applications, is less studied. In this project, we propose to apply active learning (AL) and sampling strategy into the FL framework to reduce the annotation workload. We expect that the AL and FL can improve the performance of each other complementarily. In our proposed federated active learning (F-AL) method, the clients collaboratively implement the AL to obtain the instances which are considered as informative to FL in a distributed optimization manner. We compare the test accuracies of the global FL models using the conventional random sampling strategy, client-level separate AL (S-AL), and the proposed F-AL. We empirically demonstrate that the F-AL outperforms baseline methods in image classification tasks.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 22 of its 37 references that are in the map. First posted on arXiv 1 Feb 2022.

Builds on

22 of 37 references.

References · 22

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  3. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  4. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
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  12. 2020A Survey of Deep Active Learningcited by 547
10 more
  1. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  2. 2019Learning Loss for Active Learningcited by 335
  3. 2019Variational Adversarial Active Learningcited by 284
  4. 2020FetchSGD: Communication-Efficient Federated Learning with Sketchingcited by 178
  5. 2020Tilted Empirical Risk Minimizationcited by 103
  6. 2020Task-Aware Variational Adversarial Active Learningcited by 84
  7. 2017Deep Active Learning over the Long Tailcited by 82
  8. 2020State-Relabeling Adversarial Active Learningcited by 60
  9. 2021MCDAL: Maximum Classifier Discrepancy for Active Learningcited by 18
  10. 2020Combining Federated and Active Learning for Communication-efficient Distributed Failure Prediction in Aeronauticscited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 3; 2024: 3; 2025: 2; 2026: 2.

13 papers.

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  1. 2023Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shiftscited by 26
  2. 2022Knowledge-Aware Federated Active Learning with Non-IID Datacited by 15
  3. 2023Re-thinking Federated Active Learning based on Inter-class Diversitycited by 13
  4. 2022The Future of Consumer Edge-AI Computingcited by 12
  5. 2022Federated Learning with Uncertainty via Distilled Predictive Distributionscited by 11
  6. 2025FAST: Federated Active Learning with Foundation Models for Communication-efficient Sampling and Trainingcited by 2
  7. 2023Federated Active Learning for Target Domain Generalisationcited by 0
  8. 2024Learning to Project for Cross-Task Knowledge Distillationcited by 0
  9. 2024Differentially Private Active Learning: Balancing Effective Data Selection and Privacycited by 0
  10. 2024Federated Learning with Workload Reduction through Partial Training of Client Models and Entropy-Based Data Selectioncited by 0
  11. 2025Federated Learning Inspired Fuzzy Systems: Decentralized Rule Updating for Privacy and Scalable Decision Makingcited by 0
  12. 2026Federated Active Learning Under Extreme Non-IID and Global Class Imbalancecited by 0
1 more
  1. 2026Coordination on a Budget: Federated Active Learning with Few Labelscited by 0