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The Future of Digital Health with Federated Learning

arXiv 2003.08119 ↗First posted 18 Mar 2020cs.CY cs.LGcited by 625

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Abstract (arXiv)

Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However, without access to sufficient data, ML will be prevented from reaching its full potential and, ultimately, from making the transition from research to clinical practice. This paper considers key factors contributing to this issue, explores how Federated Learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.

Key facts. Cited by 625 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Mar 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 76; 2022: 112; 2023: 117; 2024: 109; 2025: 107; 2026: 104.

625 papers.

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  1. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  2. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 507
  3. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 349
  4. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 284
  5. 2021Trustworthy AI: From Principles to Practicescited by 206
  6. 2022Medical Image Segmentation Review: The success of U-Netcited by 190
  7. 2021Federated Learning for Smart Healthcare: A Surveycited by 182
  8. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 121
  9. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 112
  10. 2021Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learningcited by 111
  11. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  12. 2021Trustworthy AI: A Computational Perspectivecited by 93
38 more
  1. 2021MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluationcited by 88
  2. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  3. 2022Federated Learning Enables Big Data for Rare Cancer Boundary Detectioncited by 83
  4. 2022NVIDIA FLARE: Federated Learning from Simulation to Real-Worldcited by 78
  5. 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
  6. 2023FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learningcited by 70
  7. 2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
  8. 2021HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Imagescited by 62
  9. 2023FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcarecited by 59
  10. 2021OpenFL: An open-source framework for Federated Learningcited by 58
  11. 2021Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edgecited by 56
  12. 2021Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learningcited by 53
  13. 2023Federated Continual Learning via Knowledge Fusion: A Surveycited by 53
  14. 2023Fair Federated Medical Image Segmentation via Client Contribution Estimationcited by 50
  15. 2021The Federated Tumor Segmentation (FeTS) Challengecited by 49
  16. 2024Byzantine-Robust Decentralized Federated Learningcited by 47
  17. 2021Learning Disentangled Representations in the Imaging Domaincited by 46
  18. 2023Federated Conformal Predictors for Distributed Uncertainty Quantificationcited by 46
  19. 2022Do Gradient Inversion Attacks Make Federated Learning Unsafe?cited by 44
  20. 2022Federated Learning via Inexact ADMMcited by 44
  21. 2021Federated Semi-supervised Medical Image Classification via Inter-client Relation Matchingcited by 42
  22. 2022Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imagingcited by 39
  23. 2021Deep Learning-based Patient Re-identification Is able to Exploit the Biometric Nature of Medical Chest X-ray Datacited by 38
  24. 2022Mechanisms that Incentivize Data Sharing in Federated Learningcited by 38
  25. 2022Can segmentation models be trained with fully synthetically generated data?cited by 37
  26. 2022MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcarecited by 36
  27. 2021Specificity-Preserving Federated Learning for MR Image Reconstructioncited by 35
  28. 2022Federated Learning under Distributed Concept Driftcited by 35
  29. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 34
  30. 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
  31. 2021Federated Contrastive Learning for Decentralized Unlabeled Medical Imagescited by 33
  32. 2022Federated Learning of Generative Image Priors for MRI Reconstructioncited by 33
  33. 2022Trading Off Privacy, Utility and Efficiency in Federated Learningcited by 33
  34. 2021Applications and Techniques for Fast Machine Learning in Sciencecited by 32
  35. 2022Federated Contrastive Learning for Volumetric Medical Image Segmentationcited by 32
  36. 2023Artificial General Intelligence for Medical Imaging Analysiscited by 32
  37. 2022On the Convergence of Clustered Federated Learningcited by 31
  38. 2022Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Surveycited by 31

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