mlsanityData to 7 Oct 2026

Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges

arXiv 2211.07893 ↗First posted 15 Nov 2022cs.LG cs.AI cs.CRcited by 34

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Abstract (arXiv)

Federated learning is the process of developing machine learning models over datasets distributed across data centers such as hospitals, clinical research labs, and mobile devices while preventing data leakage. This survey examines previous research and studies on federated learning in the healthcare sector across a range of use cases and applications. Our survey shows what challenges, methods, and applications a practitioner should be aware of in the topic of federated learning. This paper aims to lay out existing research and list the possibilities of federated learning for healthcare industries.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). Builds on 51 of its 133 references that are in the map. First posted on arXiv 15 Nov 2022.

Builds on

51 of 133 references.

References · 51

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2013Intriguing properties of neural networkscited by 5,114
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  5. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  6. 2020The Future of Digital Health with Federated Learningcited by 625
  7. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  8. 2020Federated Learning with Matched Averagingcited by 564
  9. 2022MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answeringcited by 525
  10. 2019Federated Machine Learning: Concept and Applicationscited by 409
  11. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  12. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 202
38 more
  1. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
  2. 2021Federated Learning for Smart Healthcare: A Surveycited by 182
  3. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 174
  4. 2020Threats to Federated Learning: A Surveycited by 171
  5. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  6. 2019Federated Evaluation of On-device Personalizationcited by 147
  7. 2020Salvaging Federated Learning by Local Adaptationcited by 142
  8. 2020Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Resultscited by 122
  9. 2018A generic framework for privacy preserving deep learningcited by 103
  10. 2019Secure Federated Matrix Factorizationcited by 102
  11. 2019Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregationcited by 99
  12. 2020Communication-Efficient Edge AI: Algorithms and Systemscited by 95
  13. 2016Towards the Science of Security and Privacy in Machine Learningcited by 89
  14. 2018Privacy Preserving Machine Learning: Threats and Solutionscited by 78
  15. 2020Secure and Robust Machine Learning for Healthcare: A Surveycited by 77
  16. 2019Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Recordscited by 70
  17. 2020Federated Learning for Breast Density Classification: A Real-World Implementationcited by 62
  18. 2020FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoringcited by 61
  19. 2021Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edgecited by 56
  20. 2019Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinatorcited by 51
  21. 2019Federated Forestcited by 49
  22. 2018Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Datacited by 46
  23. 2020From Federated Learning to Federated Neural Architecture Search: A Surveycited by 42
  24. 2020Federated Transfer Learning for EEG Signal Classificationcited by 36
  25. 2019Central Server Free Federated Learning over Single-sided Trust Social Networkscited by 33
  26. 2021FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Datacited by 31
  27. 2018No Peek: A Survey of private distributed deep learningcited by 30
  28. 2020Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Searchcited by 30
  29. 2020Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japancited by 23
  30. 2020Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacycited by 22
  31. 2019Privacy-Preserving Gradient Boosting Decision Treescited by 20
  32. 2021Architectural Patterns for the Design of Federated Learning Systemscited by 17
  33. 2020Pay Attention to the cough: Early Diagnosis of COVID-19 using Interpretable Symptoms Embeddings with Cough Sound Signal Processingcited by 13
  34. 2021Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approachcited by 12
  35. 2018FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Recordcited by 11
  36. 2020Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Preventioncited by 11
  37. 2020Group Knowledge Transfer: Federated Learning of Large CNNs at the Edgecited by 10
  38. 2019Pi-PE: A Pipeline for Pulmonary Embolism Detection using Sparsely Annotated 3D CT Imagescited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 16; 2025: 9; 2026: 3.

34 papers.

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  1. 2024Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classificationcited by 7
  2. 2024Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioningcited by 6
  3. 2024Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gaincited by 5
  4. 2024Worldwide Federated Training of Language Modelscited by 5
  5. 2023Privacy-preserving machine learning for healthcare: open challenges and future perspectivescited by 4
  6. 2024CollaFuse: Collaborative Diffusion Modelscited by 4
  7. 2025Revisit the Stability of Vanilla Federated Learning Under Diverse Conditionscited by 4
  8. 2025Towards Harnessing the Collaborative Power of Large and Small Models for Domain Taskscited by 4
  9. 2024Federated Learning and Differential Privacy Techniques on Multi-hospital Population-scale Electrocardiogram Datacited by 3
  10. 2024Federated and Transfer Learning for Cancer Detection Based on Image Analysiscited by 3
  11. 2024FedBaF: Federated Learning Aggregation Biased by a Foundation Modelcited by 3
  12. 2024Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data Sourcescited by 3
22 more
  1. 2025Debunking Optimization Myths in Federated Learning for Medical Image Classificationcited by 3
  2. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  3. 2023A Federated Learning Benchmark for Drug-Target Interactioncited by 2
  4. 2023FedAVO: Improving Communication Efficiency in Federated Learning with African Vultures Optimizercited by 2
  5. 2024Privacy Preserved Blood Glucose Level Cross-Prediction: An Asynchronous Decentralized Federated Learning Approachcited by 2
  6. 2024Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Reviewcited by 1
  7. 2024Federating Dynamic Models using Early-Exit Architectures for Automatic Speech Recognition on Heterogeneous Clientscited by 1
  8. 2025Zero-Shot Visual Deepfake Detection: Can AI Predict and Prevent Fake Content Before It's Created?cited by 1
  9. 2026Guarding the Middle: Protecting Intermediate Representations in Federated Split Learningcited by 1
  10. 2026Centralized vs Decentralized Federated Learning: A trade-off performance analysiscited by 1
  11. 2023Intelligent Client Selection for Federated Learning using Cellular Automatacited by 0
  12. 2023CLIFT: Analysing Natural Distribution Shift on Question Answering Models in Clinical Domaincited by 0
  13. 2023Serverless Federated Learning with flwr-serverlesscited by 0
  14. 2024Distribution-Free Fair Federated Learning with Small Samplescited by 0
  15. 2024A Multivocal Literature Review on Privacy and Fairness in Federated Learningcited by 0
  16. 2024Embedding Byzantine Fault Tolerance into Federated Learning via Consistency Scoringcited by 0
  17. 2024A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applicationscited by 0
  18. 2025Distributionally Robust Federated Learning: An ADMM Algorithmcited by 0
  19. 2025Federated Learning on Stochastic Neural Networkscited by 0
  20. 2025X-VFL: A New Vertical Federated Learning Framework with Cross Completion and Decision Subspace Alignmentcited by 0
  21. 2025Federative ischemic stroke segmentation as alternative to overcome domain-shift multi-institution challengescited by 0
  22. 2026Beyond Fixed Rounds: Data-Free Early Stopping for Practical Federated Learningcited by 0