mlsanityData to 14 Oct 2026

Federated Learning for Large Models in Medical Imaging: A Comprehensive Review

arXiv 2508.20414 ↗First posted 28 Aug 2025cs.CR cs.CVcited by 2

Not on the map. Why?

Abstract (arXiv)

Artificial intelligence (AI) has demonstrated considerable potential in the realm of medical imaging. However, the development of high-performance AI models typically necessitates training on large-scale, centralized datasets. This approach is confronted with significant challenges due to strict patient privacy regulations and legal restrictions on data sharing and utilization. These limitations hinder the development of large-scale models in medical domains and impede continuous updates and training with new data. Federated Learning (FL), a privacy-preserving distributed training framework, offers a new solution by enabling collaborative model development across fragmented medical datasets. In this survey, we review FL's contributions at two stages of the full-stack medical analysis pipeline. First, in upstream tasks such as CT or MRI reconstruction, FL enables joint training of robust reconstruction networks on diverse, multi-institutional datasets, alleviating data scarcity while preserving confidentiality. Second, in downstream clinical tasks like tumor diagnosis and segmentation, FL supports continuous model updating by allowing local fine-tuning on new data without centralizing sensitive images. We comprehensively analyze FL implementations across the medical imaging pipeline, from physics-informed reconstruction networks to diagnostic AI systems, highlighting innovations that improve communication efficiency, align heterogeneous data, and ensure secure parameter aggregation. Meanwhile, this paper provides an outlook on future research directions, aiming to serve as a valuable reference for the field's development.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 64 of its 141 references that are in the map. First posted on arXiv 28 Aug 2025.

Builds on

64 of 141 references.

References · 64

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2020Knowledge Distillation: A Surveycited by 1,603
  5. 2020Adaptive Federated Optimizationcited by 1,046
  6. 2019Deep Leakage from Gradientscited by 1,021
  7. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  8. 2020What Makes for Good Views for Contrastive Learning?cited by 846
  9. 2021Model-Contrastive Federated Learningcited by 766
  10. 2017Dynamic Routing Between Capsulescited by 689
  11. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 571
  12. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 383
38 more
  1. 2020SplitFed: When Federated Learning Meets Split Learningcited by 310
  2. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 287
  3. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 252
  4. 2017Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)cited by 224
  5. 2021Personalized Federated Learning using Hypernetworkscited by 212
  6. 2023Data-centric Artificial Intelligence: A Surveycited by 203
  7. 2021FedKD: Communication Efficient Federated Learning via Knowledge Distillationcited by 187
  8. 2017Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Losscited by 162
  9. 2017A Survey on Homomorphic Encryption Schemes: Theory and Implementationcited by 156
  10. 2020Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Resultscited by 122
  11. 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
  12. 2020Byzantine-Resilient Secure Federated Learningcited by 93
  13. 2021Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learningcited by 78
  14. 2023Federated Learning for Medical Image Analysis: A Surveycited by 78
  15. 2020Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imagingcited by 57
  16. 2021Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edgecited by 56
  17. 2022FedAvg with Fine Tuning: Local Updates Lead to Representation Learningcited by 56
  18. 2023Fair Federated Medical Image Segmentation via Client Contribution Estimationcited by 52
  19. 2022Membership Inference Attacks and Defenses in Neural Network Pruningcited by 45
  20. 2023Learning Federated Visual Prompt in Null Space for MRI Reconstructioncited by 40
  21. 2022FedTP: Federated Learning by Transformer Personalizationcited by 36
  22. 2021Specificity-Preserving Federated Learning for MR Image Reconstructioncited by 35
  23. 2022Federated Learning of Generative Image Priors for MRI Reconstructioncited by 33
  24. 2023MeLo: Low-rank Adaptation is Better than Fine-tuning for Medical Image Diagnosiscited by 33
  25. 2020Intellectual Property Protection for Deep Learning Models: Taxonomy, Methods, Attacks, and Evaluationscited by 31
  26. 2022Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Surveycited by 31
  27. 2018Capsules for Object Segmentationcited by 30
  28. 2022One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesiscited by 22
  29. 2024A Survey on Trustworthiness in Foundation Models for Medical Image Analysiscited by 17
  30. 2022Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A surveycited by 14
  31. 2022Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imagingcited by 14
  32. 2025Federated Continual Learning: Concepts, Challenges, and Solutionscited by 13
  33. 2020Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learningcited by 11
  34. 2022BlazeNeo: Blazing fast polyp segmentation and neoplasm detectioncited by 10
  35. 2024Distributed Federated Learning-Based Deep Learning Model for Privacy MRI Brain Tumor Detectioncited by 9
  36. 2025Adapting Vision Foundation Models for Real-time Ultrasound Image Segmentationcited by 9
  37. 2024Real World Federated Learning with a Knowledge Distilled Transformer for Cardiac CT Imagingcited by 8
  38. 2023Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verificationcited by 6

50 most cited of 64 All 64 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Toward Federated Large Language Models in Medicine: A Parameter-Efficient Framework for Privacy-Preserving, Multi-Institutional Adaptationcited by 1
  2. 2026MORPHFED: Federated Learning for Cross-institutional Blood Morphology Analysiscited by 0