mlsanityData to 7 Oct 2026

FedCon: A Contrastive Framework for Federated Semi-Supervised Learning

arXiv 2109.04533 ↗First posted 9 Sep 2021cs.LGcited by 11

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Abstract (arXiv)

Federated Semi-Supervised Learning (FedSSL) has gained rising attention from both academic and industrial researchers, due to its unique characteristics of co-training machine learning models with isolated yet unlabeled data. Most existing FedSSL methods focus on the classical scenario, i.e, the labeled and unlabeled data are stored at the client side. However, in real world applications, client users may not provide labels without any incentive. Thus, the scenario of labels at the server side is more practical. Since unlabeled data and labeled data are decoupled, most existing FedSSL approaches may fail to deal with such a scenario. To overcome this problem, in this paper, we propose FedCon, which introduces a new learning paradigm, i.e., contractive learning, to FedSSL. Experimental results on three datasets show that FedCon achieves the best performance with the contractive framework compared with state-of-the-art baselines under both IID and Non-IID settings. Besides, ablation studies demonstrate the characteristics of the proposed FedCon framework.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 25 of its 36 references that are in the map. First posted on arXiv 9 Sep 2021.

Builds on

25 of 36 references.

References · 25

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,712
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,080
  6. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,390
  7. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  8. 2018Federated Learning with Non-IID Datacited by 1,099
  9. 2019Unsupervised Data Augmentation for Consistency Trainingcited by 1,057
  10. 2016Temporal Ensembling for Semi-Supervised Learningcited by 1,054
  11. 2017Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learningcited by 918
  12. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
13 more
  1. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
  2. 2015Semi-Supervised Learning with Ladder Networkscited by 246
  3. 2016Practical Secure Aggregation for Federated Learning on User-Held Datacited by 178
  4. 2018Expanding the Reach of Federated Learning by Reducing Client Resource Requirementscited by 162
  5. 2018Federated Meta-Learning with Fast Convergence and Efficient Communicationcited by 154
  6. 2018Protection Against Reconstruction and Its Applications in Private Federated Learningcited by 127
  7. 2020Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learningcited by 112
  8. 2018There Are Many Consistent Explanations of Unlabeled Data: Why You Should Averagecited by 82
  9. 2017Adversarial Dropout for Supervised and Semi-supervised Learningcited by 38
  10. 2020Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospectivecited by 27
  11. 2020Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Modelscited by 10
  12. 2018LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care datacited by 9
  13. 2020Exploiting Unlabeled Data in Smart Cities using Federated Learningcited by 6

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Cited by year

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

11 papers.

  1. 2022Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phasescited by 18
  2. 2023Contrastive encoder pre-training-based clustered federated learning for heterogeneous datacited by 5
  3. 2024(FL)2: Overcoming Few Labels in Federated Semi-Supervised Learningcited by 5
  4. 2023When does the student surpass the teacher? Federated Semi-supervised Learning with Teacher-Student EMAcited by 4
  5. 2023MrTF: Model Refinery for Transductive Federated Learningcited by 2
  6. 2024FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clientscited by 2
  7. 2025CATCHFed: Efficient Unlabeled Data Utilization for Semi-Supervised Federated Learning in Limited Labels Environmentscited by 1
  8. 2022Uncertainty Minimization for Personalized Federated Semi-Supervised Learningcited by 0
  9. 2024Unsupervised Federated Optimization at the Edge: D2D-Enabled Learning without Labelscited by 0
  10. 2025Unified Alignment Protocol: Making Sense of the Unlabeled Data in New Domainscited by 0
  11. 2025Semi-Supervised Federated Learning via Dual Contrastive Learning and Soft Labeling for Intelligent Fault Diagnosiscited by 0