mlsanityData to 14 Oct 2026

Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation

arXiv 2406.12815 ↗First posted 18 Jun 2024cs.LG cs.AI cs.DCcited by 9

Not on the map. Why?

Abstract (arXiv)

Machine learning (ML) and Artificial Intelligence (AI) have fueled remarkable advancements, particularly in healthcare. Within medical imaging, ML models hold the promise of improving disease diagnoses, treatment planning, and post-treatment monitoring. Various computer vision tasks like image classification, object detection, and image segmentation are poised to become routine in clinical analysis. However, privacy concerns surrounding patient data hinder the assembly of large training datasets needed for developing and training accurate, robust, and generalizable models. Federated Learning (FL) emerges as a compelling solution, enabling organizations to collaborate on ML model training by sharing model training information (gradients) rather than data (e.g., medical images). FL's distributed learning framework facilitates inter-institutional collaboration while preserving patient privacy. However, FL, while robust in privacy preservation, faces several challenges. Sensitive information can still be gleaned from shared gradients that are passed on between organizations during model training. Additionally, in medical imaging, quantifying model confidenceuncertainty accurately is crucial due to the noise and artifacts present in the data. Uncertainty estimation in FL encounters unique hurdles due to data heterogeneity across organizations. This paper offers a comprehensive review of FL, privacy preservation, and uncertainty estimation, with a focus on medical imaging. Alongside a survey of current research, we identify gaps in the field and suggest future directions for FL research to enhance privacy and address noisy medical imaging data challenges.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 56 of its 123 references that are in the map. First posted on arXiv 18 Jun 2024.

Builds on

56 of 123 references.

References · 56

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  5. 2018Neural Tangent Kernel: Convergence and Generalization in Neural Networkscited by 2,621
  6. 2018Federated Learning with Non-IID Datacited by 1,108
  7. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  8. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 690
  9. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 682
  10. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 571
  11. 2018The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scalecited by 544
  12. 2019The Liver Tumor Segmentation Benchmark (LiTS)cited by 509
38 more
  1. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 383
  2. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
  3. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 210
  4. 2020MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysiscited by 206
  5. 2022Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisonscited by 170
  6. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  7. 2017A Survey on Homomorphic Encryption Schemes: Theory and Implementationcited by 156
  8. 2013Learning by Transductioncited by 121
  9. 2019Federated Learning for Emoji Prediction in a Mobile Keyboardcited by 118
  10. 2018Federated Learning for Keyword Spottingcited by 95
  11. 2022Federated Learning Enables Big Data for Rare Cancer Boundary Detectioncited by 84
  12. 2022NVIDIA FLARE: Federated Learning from Simulation to Real-Worldcited by 80
  13. 2022Personalized Federated Learning via Variational Bayesian Inferencecited by 68
  14. 2021Understanding Softmax Confidence and Uncertaintycited by 63
  15. 2021OpenFL: An open-source framework for Federated Learningcited by 58
  16. 2021Decentralized Federated Learning through Proxy Model Sharingcited by 57
  17. 2020Federated Learning for Computational Pathology on Gigapixel Whole Slide Imagescited by 54
  18. 2021The Federated Tumor Segmentation (FeTS) Challengecited by 50
  19. 2022FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Datacited by 50
  20. 2023Federated Conformal Predictors for Distributed Uncertainty Quantificationcited by 46
  21. 2019Differential Privacy-enabled Federated Learning for Sensitive Health Datacited by 44
  22. 2021Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrixcited by 43
  23. 2021A Bayesian Federated Learning Framework with Online Laplace Approximationcited by 41
  24. 2021Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learningcited by 39
  25. 2022FedPop: A Bayesian Approach for Personalised Federated Learningcited by 35
  26. 2022Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learningcited by 30
  27. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  28. 2023Conformal Prediction for Federated Uncertainty Quantification Under Label Shiftcited by 29
  29. 2021Personalized Federated Learning with Adaptive Batchnorm for Healthcarecited by 26
  30. 2022TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernelscited by 26
  31. 2021Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learningcited by 23
  32. 2022One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesiscited by 22
  33. 2022APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learningcited by 20
  34. 2021Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryptioncited by 18
  35. 2021Distribution-Free Federated Learning with Conformal Predictionscited by 16
  36. 2022Self-Aware Personalized Federated Learningcited by 16
  37. 2022Federated Learning with Uncertainty via Distilled Predictive Distributionscited by 12
  38. 2022DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Settingcited by 12

50 most cited of 56 All 56 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 4.

9 papers.

  1. 2025Federated nnU-Net for Privacy-Preserving Medical Image Segmentationcited by 3
  2. 2024Information-Geometric Barycenters for Bayesian Federated Learningcited by 1
  3. 2025Securing Genomic Data Against Inference Attacks in Federated Learning Environmentscited by 1
  4. 2026From Redaction to Restoration: Deep Learning for Medical Image Anonymization and Reconstructioncited by 1
  5. 2025FIVA: Federated Inverse Variance Averaging for Universal CT Segmentation with Uncertainty Estimationcited by 0
  6. 2025ParaBlock: Communication-Computation Parallel Block Coordinate Federated Learning for Large Language Modelscited by 0
  7. 2026Federated Learning for Cross-Modality Medical Image Segmentation via Augmentation-Driven Generalizationcited by 0
  8. 2026SCOPE: Semantic Coreset with Orthogonal Projection Embeddings for Federated learningcited by 0
  9. 2026Representation learning from OCT imagescited by 0