mlsanityData to 7 Oct 2026

Recent Methodological Advances in Federated Learning for Healthcare

arXiv 2310.02874 ↗First posted 4 Oct 2023cs.LG cs.AIcited by 11

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Abstract (arXiv)

For healthcare datasets, it is often not possible to combine data samples from multiple sites due to ethical, privacy or logistical concerns. Federated learning allows for the utilisation of powerful machine learning algorithms without requiring the pooling of data. Healthcare data has many simultaneous challenges which require new methodologies to address, such as highly-siloed data, class imbalance, missing data, distribution shifts and non-standardised variables. Federated learning adds significant methodological complexity to conventional centralised machine learning, requiring distributed optimisation, communication between nodes, aggregation of models and redistribution of models. In this systematic review, we consider all papers on Scopus that were published between January 2015 and February 2023 and which describe new federated learning methodologies for addressing challenges with healthcare data. We performed a detailed review of the 89 papers which fulfilled these criteria. Significant systemic issues were identified throughout the literature which compromise the methodologies in many of the papers reviewed. We give detailed recommendations to help improve the quality of the methodology development for federated learning in healthcare.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 48 of its 155 references that are in the map. First posted on arXiv 4 Oct 2023.

Builds on

48 of 155 references.

References · 48

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  4. 2018How To Backdoor Federated Learningcited by 745
  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. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  8. 2020Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scanscited by 158
  9. 2017On the convergence properties of a K-step averaging stochastic gradient descent algorithm for nonconvex optimizationcited by 91
  10. 2022NVIDIA FLARE: Federated Learning from Simulation to Real-Worldcited by 78
  11. 2020Siloed Federated Learning for Multi-Centric Histopathology Datasetscited by 61
  12. 2020FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoringcited by 61
36 more
  1. 2021OpenFL: An open-source framework for Federated Learningcited by 58
  2. 2020Mitigating Bias in Federated Learningcited by 56
  3. 2019On Safeguarding Privacy and Security in the Framework of Federated Learningcited by 55
  4. 2021FLOP: Federated Learning on Medical Datasets using Partial Networkscited by 42
  5. 2020FOCUS: Dealing with Label Quality Disparity in Federated Learningcited by 36
  6. 2021Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AIcited by 26
  7. 2021Personalized Federated Learning with Adaptive Batchnorm for Healthcarecited by 26
  8. 2023Learning from data with structured missingnesscited by 24
  9. 2022Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learningcited by 21
  10. 2022Classification of datasets with imputed missing values: does imputation quality matter?cited by 21
  11. 2021Multi-task Federated Learning for Heterogeneous Pancreas Segmentationcited by 19
  12. 2021Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Trainingcited by 17
  13. 2021Hierarchical Federated Learning based Anomaly Detection using Digital Twins for Smart Healthcarecited by 15
  14. 2022FedStack: Personalized activity monitoring using stacked federated learningcited by 15
  15. 2021Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversarycited by 14
  16. 2021Privacy and Trust Redefined in Federated Machine Learningcited by 13
  17. 2021Federated Multi-View Learning for Private Medical Data Integration and Analysiscited by 12
  18. 2022Semi-FedSER: Semi-supervised Learning for Speech Emotion Recognition On Federated Learning using Multiview Pseudo-Labelingcited by 12
  19. 2022Multi-Task Distributed Learning using Vision Transformer with Random Patch Permutationcited by 10
  20. 2022HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learningcited by 10
  21. 2021Handling Data Heterogeneity with Generative Replay in Collaborative Learning for Medical Imagingcited by 9
  22. 2022SecureFedYJ: a safe feature Gaussianization protocol for Federated Learningcited by 9
  23. 2019Conclave: secure multi-party computation on big data (extended TR)cited by 7
  24. 2022Federated Remote Physiological Measurement with Imperfect Datacited by 7
  25. 2021Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Applicationcited by 6
  26. 2022Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensemblingcited by 6
  27. 2022On the Design of Communication-Efficient Federated Learning for Health Monitoringcited by 6
  28. 2020MDLdroid: a ChainSGD-reduce Approach to Mobile Deep Learning for Personal Mobile Sensingcited by 5
  29. 2022Reproducibility of the Methods in Medical Imaging with Deep Learningcited by 5
  30. 2021Improving Performance of Federated Learning based Medical Image Analysis in Non-IID Settings using Image Augmentationcited by 4
  31. 2022A Federated Cox Model with Non-Proportional Hazardscited by 4
  32. 2022Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentationcited by 4
  33. 2023SMPC-based Federated Learning for 6G enabled Internet of Medical Thingscited by 4
  34. 2023CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communicationscited by 3
  35. 2022Navigating the challenges in creating complex data systems: a development philosophycited by 2
  36. 2022CEDAR: Communication Efficient Distributed Analysis for Regressionscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 5.

11 papers.

  1. 2024Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Reviewcited by 2
  2. 2025Federated Learning with Layer Skipping: Efficient Training of Large Language Models for Healthcare NLPcited by 2
  3. 2024FedMAP: Personalised Federated Learning for Real Large-Scale Healthcare Systemscited by 1
  4. 2025Federated learning, ethics, and the double black box problem in medical AIcited by 1
  5. 2026Embedding-Based Federated Learning with Runtime Governance for Iron Deficiency Predictioncited by 1
  6. 2024Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic reviewcited by 0
  7. 2025A Novel Pearson Correlation-Based Merging Algorithm for Robust Distributed Machine Learning with Heterogeneous Datacited by 0
  8. 2026Adapting Natural Language Processing Models Across Jurisdictions: A pilot Study in Canadian Cancer Registriescited by 0
  9. 2026FedSLIM: Privacy-Preserving Federated MDL-Based Descriptive Pattern Mining Across Data Siloscited by 0
  10. 2026Fed-ReMasker: Federated Tabular Imputation under Feature-Level Missingnesscited by 0
  11. 2026Making Cross-Continental Federated Learning Repeatable with FLIP: a Multi-Application Studycited by 0