mlsanityData to 7 Oct 2026

Federated Learning for Breast Density Classification: A Real-World Implementation

arXiv 2009.01871 ↗First posted 3 Sep 2020eess.IV cs.CVcited by 62

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Abstract (arXiv)

Building robust deep learning-based models requires large quantities of diverse training data. In this study, we investigate the use of federated learning (FL) to build medical imaging classification models in a real-world collaborative setting. Seven clinical institutions from across the world joined this FL effort to train a model for breast density classification based on Breast Imaging, Reporting & Data System (BI-RADS). We show that despite substantial differences among the datasets from all sites (mammography system, class distribution, and data set size) and without centralizing data, we can successfully train AI models in federation. The results show that models trained using FL perform 6.3% on average better than their counterparts trained on an institute's local data alone. Furthermore, we show a 45.8% relative improvement in the models' generalizability when evaluated on the other participating sites' testing data.

Key facts. Cited by 62 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Sep 2020.

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Citing papers per year: 2021: 19; 2022: 16; 2023: 12; 2024: 6; 2025: 8; 2026: 1.

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  1. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 284
  2. 2021Federated Learning for Smart Healthcare: A Surveycited by 182
  3. 2021Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learningcited by 111
  4. 2022Federated Learning Enables Big Data for Rare Cancer Boundary Detectioncited by 83
  5. 2022NVIDIA FLARE: Federated Learning from Simulation to Real-Worldcited by 78
  6. 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
  7. 2021HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Imagescited by 62
  8. 2021The Federated Tumor Segmentation (FeTS) Challengecited by 49
  9. 2022Do Gradient Inversion Attacks Make Federated Learning Unsafe?cited by 44
  10. 2021Federated Semi-supervised Medical Image Classification via Inter-client Relation Matchingcited by 42
  11. 2021Specificity-Preserving Federated Learning for MR Image Reconstructioncited by 35
  12. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 34
38 more
  1. 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
  2. 2022Federated Learning of Generative Image Priors for MRI Reconstructioncited by 33
  3. 2022CrowdGuard: Federated Backdoor Detection in Federated Learningcited by 26
  4. 2021Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learningcited by 25
  5. 2022One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesiscited by 22
  6. 2023Federated Learning for Computer Visioncited by 22
  7. 2022IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentationcited by 21
  8. 2021Memory-aware curriculum federated learning for breast cancer classificationcited by 20
  9. 2021Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentationcited by 19
  10. 2021Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Diseasecited by 19
  11. 2021Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryptioncited by 18
  12. 2022Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentationcited by 16
  13. 2022Federated Multi-organ Segmentation with Inconsistent Labelscited by 15
  14. 2022Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalancecited by 13
  15. 2024The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscapecited by 13
  16. 2021Addressing catastrophic forgetting for medical domain expansioncited by 12
  17. 2021FedSLD: Federated Learning with Shared Label Distribution for Medical Image Classificationcited by 10
  18. 2023Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directionscited by 10
  19. 2023Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applicationscited by 10
  20. 2021Handling Data Heterogeneity with Generative Replay in Collaborative Learning for Medical Imagingcited by 9
  21. 2022Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray Datacited by 8
  22. 2022Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicinecited by 8
  23. 2024FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injectioncited by 8
  24. 2021FedDropoutAvg: Generalizable federated learning for histopathology image classificationcited by 7
  25. 2023Understanding Deep Gradient Leakage via Inversion Influence Functionscited by 6
  26. 2025Federated Learning for Medical Image Classification: A Comprehensive Benchmarkcited by 6
  27. 2021Semi-Supervised Federated Peer Learning for Skin Lesion Classificationcited by 4
  28. 2021Federated Learning: Issues in Medical Applicationcited by 4
  29. 2023On the Effect of Defections in Federated Learning and How to Prevent Themcited by 4
  30. 2024Empowering Federated Learning for Massive Models with NVIDIA FLAREcited by 4
  31. 2024Federated and Transfer Learning for Cancer Detection Based on Image Analysiscited by 3
  32. 2022CPS Attack Detection under Limited Local Information in Cyber Security: A Multi-node Multi-class Classification Ensemble Approachcited by 2
  33. 2025Technical and Legal Aspects of Federated Learning in Bioinformatics: Applications, Challenges and Opportunitiescited by 2
  34. 2023Welfare and Fairness Dynamics in Federated Learning: A Client Selection Perspectivecited by 1
  35. 2023Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imagingcited by 1
  36. 2023Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigationscited by 1
  37. 2024Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Reviewcited by 1
  38. 2025Federated learning, ethics, and the double black box problem in medical AIcited by 1

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