mlsanityData to 7 Oct 2026

Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective

arXiv 2002.11545 ↗First posted 26 Feb 2020cs.LG stat.MLcited by 27

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Abstract (arXiv)

Federated Learning (FL) proposed in recent years has received significant attention from researchers in that it can bring separate data sources together and build machine learning models in a collaborative but private manner. Yet, in most applications of FL, such as keyboard prediction, labeling data requires virtually no additional efforts, which is not generally the case. In reality, acquiring large-scale labeled datasets can be extremely costly, which motivates research works that exploit unlabeled data to help build machine learning models. However, to the best of our knowledge, few existing works aim to utilize unlabeled data to enhance federated learning, which leaves a potentially promising research topic. In this paper, we identify the need to exploit unlabeled data in FL, and survey possible research fields that can contribute to the goal.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Feb 2020.

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Citing papers per year: 2021: 7; 2022: 9; 2023: 5; 2024: 6.

27 papers.

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  1. 2022Divergence-aware Federated Self-Supervised Learningcited by 71
  2. 2021Collaborative Unsupervised Visual Representation Learning from Decentralized Datacited by 68
  3. 2021SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Trainingcited by 45
  4. 2022Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phasescited by 18
  5. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
  6. 2021FedCon: A Contrastive Framework for Federated Semi-Supervised Learningcited by 11
  7. 2024Trustworthy Federated Learning: Privacy, Security, and Beyondcited by 11
  8. 2022Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Datacited by 9
  9. 2023Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulatorscited by 9
  10. 2022Federated Semi-Supervised Learning with Prototypical Networkscited by 8
  11. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  12. 2022Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Dronescited by 7
15 more
  1. 2024Federated Unsupervised Domain Generalization using Global and Local Alignment of Gradientscited by 7
  2. 2022Privacy-preserving Speech Emotion Recognition through Semi-Supervised Federated Learningcited by 6
  3. 2023Towards Unbiased Training in Federated Open-world Semi-supervised Learningcited by 6
  4. 2021Federated Self-Training for Semi-Supervised Audio Recognitioncited by 5
  5. 2021FedSEAL: Semi-Supervised Federated Learning with Self-Ensemble Learning and Negative Learningcited by 4
  6. 2023Navigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object Detectioncited by 4
  7. 2021Towards Communication-Efficient and Privacy-Preserving Federated Representation Learningcited by 3
  8. 2021Semi-Supervised Federated Learning with non-IID Data: Algorithm and System Designcited by 3
  9. 2022TinyMLOps: Operational Challenges for Widespread Edge AI Adoptioncited by 3
  10. 2023FedIL: Federated Incremental Learning from Decentralized Unlabeled Data with Convergence Analysiscited by 3
  11. 2024FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clientscited by 2
  12. 2022Feature Correlation-guided Knowledge Transfer for Federated Self-supervised Learningcited by 1
  13. 2023Inclusive Data Representation in Federated Learning: A Novel Approach Integrating Textual and Visual Promptcited by 1
  14. 2024Learn How to Query from Unlabeled Data Streams in Federated Learningcited by 1
  15. 2024SemiDFL: A Semi-Supervised Paradigm for Decentralized Federated Learningcited by 0