mlsanityData to 13 Oct 2026

Specificity-Preserving Federated Learning for MR Image Reconstruction

arXiv 2112.05752 ↗First posted 9 Dec 2021eess.IV cs.CVcited by 35

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Abstract (arXiv)

Federated learning (FL) can be used to improve data privacy and efficiency in magnetic resonance (MR) image reconstruction by enabling multiple institutions to collaborate without needing to aggregate local data. However, the domain shift caused by different MR imaging protocols can substantially degrade the performance of FL models. Recent FL techniques tend to solve this by enhancing the generalization of the global model, but they ignore the domain-specific features, which may contain important information about the device properties and be useful for local reconstruction. In this paper, we propose a specificity-preserving FL algorithm for MR image reconstruction (FedMRI). The core idea is to divide the MR reconstruction model into two parts: a globally shared encoder to obtain a generalized representation at the global level, and a client-specific decoder to preserve the domain-specific properties of each client, which is important for collaborative reconstruction when the clients have unique distribution. Such scheme is then executed in the frequency space and the image space respectively, allowing exploration of generalized representation and client-specific properties simultaneously in different spaces. Moreover, to further boost the convergence of the globally shared encoder when a domain shift is present, a weighted contrastive regularization is introduced to directly correct any deviation between the client and server during optimization. Extensive experiments demonstrate that our FedMRI's reconstructed results are the closest to the ground-truth for multi-institutional data, and that it outperforms state-of-the-art FL methods.

Key facts. Cited by 35 papers in the map (data through 13 Oct 2026). Builds on 48 of its 80 references that are in the map. First posted on arXiv 9 Dec 2021.

Builds on

48 of 80 references.

References · 48

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36 more
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  15. 2020Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Resultscited by 122
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  17. 2017Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstructioncited by 105
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Cited by year (2026 to 13 Oct)

Citing papers per year: 2022: 6; 2023: 13; 2024: 4; 2025: 10; 2026: 2.

35 papers.

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  1. 2022FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correctioncited by 181
  2. 2023Federated Learning for Medical Image Analysis: A Surveycited by 78
  3. 2023Learning Federated Visual Prompt in Null Space for MRI Reconstructioncited by 40
  4. 2022Federated Learning of Generative Image Priors for MRI Reconstructioncited by 33
  5. 2023Federated Domain Generalization: A Surveycited by 25
  6. 2022One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesiscited by 22
  7. 2022Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imagingcited by 14
  8. 2023Multimodal Federated Learning in Healthcare: a Reviewcited by 13
  9. 2022Federated Cross Learning for Medical Image Segmentationcited by 9
  10. 2023Cross-Modal Vertical Federated Learning for MRI Reconstructioncited by 9
  11. 2023Towards Instance-adaptive Inference for Federated Learningcited by 7
  12. 2024Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced Modalitiescited by 6
23 more
  1. 2025Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language Modelcited by 6
  2. 2023Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstructioncited by 5
  3. 2023Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstructioncited by 5
  4. 2023Rethinking Client Drift in Federated Learning: A Logit Perspectivecited by 4
  5. 2025Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstructioncited by 4
  6. 2022Analysing the Influence of Attack Configurations on the Reconstruction of Medical Images in Federated Learningcited by 3
  7. 2023FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoisingcited by 3
  8. 2023ColonNeRF: High-Fidelity Neural Reconstruction of Long Colonoscopycited by 2
  9. 2024A New Perspective to Boost Performance Fairness for Medical Federated Learningcited by 2
  10. 2025Enhancing the Convergence of Federated Learning Aggregation Strategies with Limited Datacited by 2
  11. 2025Physics-Inspired Distributed Radio Map Estimationcited by 2
  12. 2025Federated Learning for Large Models in Medical Imaging: A Comprehensive Reviewcited by 2
  13. 2023Reliable Federated Disentangling Network for Non-IID Domain Featurecited by 1
  14. 2023FAM: fast adaptive federated meta-learningcited by 1
  15. 2026SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Rankscited by 1
  16. 2024HF-Fed: Hierarchical based customized Federated Learning Framework for X-Ray Imagingcited by 0
  17. 2024FedCAR: Cross-client Adaptive Re-weighting for Generative Models in Federated Learningcited by 0
  18. 2025Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integrationcited by 0
  19. 2025FedVSR: Towards Model-Agnostic Federated Learning in Video Super-Resolutioncited by 0
  20. 2025FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentationcited by 0
  21. 2025PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstructioncited by 0
  22. 2025Improving Learning of New Diseases through Knowledge-Enhanced Initialization for Federated Adapter Tuningcited by 0
  23. 2026Projection Guided Personalized Federated Learning for Low Dose CT Denoisingcited by 0