mlsanityData to 14 Oct 2026

Federated Learning without Full Labels: A Survey

arXiv 2303.14453 ↗First posted 25 Mar 2023cs.LG cs.AIcited by 13

Not on the map. Why?

Abstract (arXiv)

Data privacy has become an increasingly important concern in real-world big data applications such as machine learning. To address the problem, federated learning (FL) has been a promising solution to building effective machine learning models from decentralized and private data. Existing federated learning algorithms mainly tackle the supervised learning problem, where data are assumed to be fully labeled. However, in practice, fully labeled data is often hard to obtain, as the participants may not have sufficient domain expertise, or they lack the motivation and tools to label data. Therefore, the problem of federated learning without full labels is important in real-world FL applications. In this paper, we discuss how the problem can be solved with machine learning techniques that leverage unlabeled data. We present a survey of methods that combine FL with semi-supervised learning, self-supervised learning, and transfer learning methods. We also summarize the datasets used to evaluate FL methods without full labels. Finally, we highlight future directions in the context of FL without full labels.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 64 of its 92 references that are in the map. First posted on arXiv 25 Mar 2023.

Builds on

64 of 92 references.

References · 64

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  4. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  5. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  6. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  7. 2017Mask R-CNNcited by 7,277
  8. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  9. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  10. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,750
  11. 2016Pointer Sentinel Mixture Modelscited by 3,096
  12. 2019Advances and Open Problems in Federated Learningcited by 3,046
38 more
  1. 2015Domain-Adversarial Training of Neural Networkscited by 3,044
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2020Exploring Simple Siamese Representation Learningcited by 2,866
  4. 2016Deep Learning with Differential Privacycited by 2,728
  5. 2016Adversarial examples in the physical worldcited by 1,769
  6. 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,591
  7. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  8. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,391
  9. 2015Learning Transferable Features with Deep Adaptation Networkscited by 1,329
  10. 2014How transferable are features in deep neural networks?cited by 1,225
  11. 2019A Comprehensive Survey on Transfer Learningcited by 1,160
  12. 2018Federated Learning with Non-IID Datacited by 1,108
  13. 2015Unsupervised Visual Representation Learning by Context Predictioncited by 1,066
  14. 2015Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Bookscited by 1,043
  15. 2017Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learningcited by 920
  16. 2016InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Netscited by 860
  17. 2018LEAF: A Benchmark for Federated Settingscited by 792
  18. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
  19. 2020Self-supervised Learning: Generative or Contrastivecited by 694
  20. 2017Maximum Classifier Discrepancy for Unsupervised Domain Adaptationcited by 683
  21. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
  22. 2019Self-supervised Visual Feature Learning with Deep Neural Networks: A Surveycited by 657
  23. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  24. 2021Towards Personalized Federated Learningcited by 464
  25. 2019Unlabeled Data Improves Adversarial Robustnesscited by 416
  26. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 395
  27. 2019AutoML: A Survey of the State-of-the-Artcited by 390
  28. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 356
  29. 2016Semi-supervised Knowledge Transfer for Deep Learning from Private Training Datacited by 348
  30. 2021Federated Learning on Non-IID Data: A Surveycited by 346
  31. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 337
  32. 2021A Field Guide to Federated Optimizationcited by 315
  33. 2018Diverse Image-to-Image Translation via Disentangled Representationscited by 296
  34. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 287
  35. 2019Are Labels Required for Improving Adversarial Robustness?cited by 163
  36. 2019Federated Adversarial Domain Adaptationcited by 140
  37. 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 136
  38. 2020Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Datacited by 125

50 most cited of 64 All 64 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 7; 2025: 3; 2026: 1.

13 papers.

Sort:

By area:

  1. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 99
  2. 2024Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimizationcited by 28
  3. 2024Rethinking the Representation in Federated Unsupervised Learning with Non-IID Datacited by 13
  4. 2024Federated Learning with Bilateral Curation for Partially Class-Disjoint Datacited by 13
  5. 2024Robust Training of Federated Models with Extremely Label Deficiencycited by 7
  6. 2023Navigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object Detectioncited by 4
  7. 2024Federated Learning for Misbehaviour Detection with Variational Autoencoders and Gaussian Mixture Modelscited by 2
  8. 2024Fair Federated Data Clustering through Personalization: Bridging the Gap between Diverse Data Distributionscited by 1
  9. 2025FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detectioncited by 1
  10. 2025PPFPL: Cross-silo Privacy-preserving Federated Prototype Learning Against Data Poisoning Attackscited by 1
  11. 2025CATCHFed: Efficient Unlabeled Data Utilization for Semi-Supervised Federated Learning in Limited Labels Environmentscited by 1
  12. 2024Federated Clustering: An Unsupervised Cluster-Wise Training for Decentralized Data Distributionscited by 0
1 more
  1. 2026Beyond Weights and Gradients: A Taxonomy of Federated Learning Messagescited by 0