mlsanityData to 7 Oct 2026

Federated Contrastive Learning for Decentralized Unlabeled Medical Images

arXiv 2109.07504 ↗First posted 15 Sep 2021cs.LG cs.CV eess.IVcited by 33

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Abstract (arXiv)

A label-efficient paradigm in computer vision is based on self-supervised contrastive pre-training on unlabeled data followed by fine-tuning with a small number of labels. Making practical use of a federated computing environment in the clinical domain and learning on medical images poses specific challenges. In this work, we propose FedMoCo, a robust federated contrastive learning (FCL) framework, which makes efficient use of decentralized unlabeled medical data. FedMoCo has two novel modules: metadata transfer, an inter-node statistical data augmentation module, and self-adaptive aggregation, an aggregation module based on representational similarity analysis. To the best of our knowledge, this is the first FCL work on medical images. Our experiments show that FedMoCo can consistently outperform FedAvg, a seminal federated learning framework, in extracting meaningful representations for downstream tasks. We further show that FedMoCo can substantially reduce the amount of labeled data required in a downstream task, such as COVID-19 detection, to achieve a reasonable performance.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). Builds on 17 of its 29 references that are in the map. First posted on arXiv 15 Sep 2021.

Builds on

17 of 29 references.

References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  3. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  4. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  5. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  6. 2018Unsupervised Feature Learning via Non-Parametric Instance-level Discriminationcited by 1,838
  7. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,410
  8. 2020What Makes for Good Views for Contrastive Learning?cited by 843
  9. 2019Self-Supervised Learning of Pretext-Invariant Representationscited by 806
  10. 2020The Future of Digital Health with Federated Learningcited by 625
  11. 2020VinDr-CXR: An open dataset of chest X-rays with radiologist's annotationscited by 236
  12. 2020Can AI help in screening Viral and COVID-19 pneumonia?cited by 200
5 more
  1. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  2. 2019Branched Multi-Task Networks: Deciding What Layers To Sharecited by 76
  3. 2020MoCo-CXR: MoCo Pretraining Improves Representation and Transferability of Chest X-ray Modelscited by 61
  4. 2020Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Imagescited by 43
  5. 2018Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratiocited by 16

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 13; 2023: 7; 2024: 5; 2025: 4; 2026: 4.

33 papers.

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  1. 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
  2. 2023FedMultimodal: A Benchmark For Multimodal Federated Learningcited by 51
  3. 2022Dissecting Self-Supervised Learning Methods for Surgical Computer Visioncited by 48
  4. 2022Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imagingcited by 39
  5. 2022FedMix: Mixed Supervised Federated Learning for Medical Image Segmentationcited by 22
  6. 2022Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phasescited by 18
  7. 2022Distributed Contrastive Learning for Medical Image Segmentationcited by 16
  8. 2023Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directionscited by 14
  9. 2022Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalancecited by 13
  10. 2022FedHarmony: Unlearning Scanner Bias with Distributed Datacited by 10
  11. 2023A Review of Predictive and Contrastive Self-supervised Learning for Medical Imagescited by 10
  12. 2022Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicinecited by 8
21 more
  1. 2024A Mutual Information Perspective on Federated Contrastive Learningcited by 7
  2. 2022CXR-FL: Deep Learning-Based Chest X-ray Image Analysis Using Federated Learningcited by 6
  3. 2022Revisiting Vicinal Risk Minimization for Partially Supervised Multi-Label Classification Under Data Scarcitycited by 5
  4. 2022VFed-SSD: Towards Practical Vertical Federated Advertisingcited by 5
  5. 2022Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosiscited by 5
  6. 2023FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoringcited by 4
  7. 2022Learning Underrepresented Classes from Decentralized Partially Labeled Medical Imagescited by 3
  8. 2023A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Imagescited by 2
  9. 2024Privacy-Preserved Taxi Demand Prediction System Utilizing Distributed Datacited by 1
  10. 2025Bridging Contrastive Learning and Domain Adaptation: Theoretical Perspective and Practical Applicationcited by 1
  11. 2025FedQuad: Federated Stochastic Quadruplet Learning to Mitigate Data Heterogeneitycited by 1
  12. 2023SFHarmony: Source Free Domain Adaptation for Distributed Neuroimaging Analysiscited by 0
  13. 2024Resource-efficient Layer-wise Federated Self-supervised Learningcited by 0
  14. 2024Non-parametric regularization for class imbalance federated medical image classificationcited by 0
  15. 2024Federated Black-Box Adaptation for Semantic Segmentationcited by 0
  16. 2025Decoupled Contrastive Learning for Federated Learningcited by 0
  17. 2025Contrastive Self-Supervised Learning at the Edge: An Energy Perspectivecited by 0
  18. 2026On-Orbit Space AI: Federated, Multi-Agent, and Collaborative Algorithms for Satellite Constellationscited by 0
  19. 2026Enhancing Federated Quadruplet Learning: Stochastic Client Selection and Embedding Stability Analysiscited by 0
  20. 2026Sheaf-Based Federated Representation Learningcited by 0
  21. 2026Coordination on a Budget: Federated Active Learning with Few Labelscited by 0