mlsanityData to 14 Oct 2026

Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation

arXiv 2203.10144 ↗First posted 18 Mar 2022cs.CVcited by 42

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Abstract (arXiv)

Cross-silo federated learning (FL) has attracted much attention in medical imaging analysis with deep learning in recent years as it can resolve the critical issues of insufficient data, data privacy, and training efficiency. However, there can be a generalization gap between the model trained from FL and the one from centralized training. This important issue comes from the non-iid data distribution of the local data in the participating clients and is well-known as client drift. In this work, we propose a novel training framework FedSM to avoid the client drift issue and successfully close the generalization gap compared with the centralized training for medical image segmentation tasks for the first time. We also propose a novel personalized FL objective formulation and a new method SoftPull to solve it in our proposed framework FedSM. We conduct rigorous theoretical analysis to guarantee its convergence for optimizing the non-convex smooth objective function. Real-world medical image segmentation experiments using deep FL validate the motivations and effectiveness of our proposed method.

Key facts. Cited by 42 papers in the map (data through 14 Oct 2026). Builds on 39 of its 51 references that are in the map. First posted on arXiv 18 Mar 2022.

Builds on

39 of 51 references.

References · 39

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
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27 more
  1. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  2. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  3. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
  4. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
  5. 2021The Medical Segmentation Decathloncited by 628
  6. 2020Personalized Federated Learning with Moreau Envelopescited by 618
  7. 2021Federated Learning Based on Dynamic Regularizationcited by 562
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  11. 2019Fair Resource Allocation in Federated Learningcited by 394
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  13. 2020Adaptive Personalized Federated Learningcited by 358
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  15. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 325
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  17. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 287
  18. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 269
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  22. 2019Variance Reduced Local SGD with Lower Communication Complexitycited by 80
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 11; 2023: 6; 2024: 12; 2025: 11; 2026: 2.

42 papers.

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  1. 2022NVIDIA FLARE: Federated Learning from Simulation to Real-Worldcited by 80
  2. 2023Federated Learning for Medical Image Analysis: A Surveycited by 78
  3. 2022Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learningcited by 35
  4. 2022Faster Adaptive Federated Learningcited by 35
  5. 2022FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Modelcited by 30
  6. 2024Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversitycited by 30
  7. 2022Adversarial Weight Perturbation Improves Generalization in Graph Neural Networkscited by 21
  8. 2023AMIGO: Sparse Multi-Modal Graph Transformer with Shared-Context Processing for Representation Learning of Giga-pixel Imagescited by 18
  9. 2022Federated Multi-organ Segmentation with Inconsistent Labelscited by 15
  10. 2024MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysiscited by 14
  11. 2024FedUV: Uniformity and Variance for Heterogeneous Federated Learningcited by 9
  12. 2025dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysiscited by 9
30 more
  1. 2022FedDAR: Federated Domain-Aware Representation Learningcited by 8
  2. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  3. 2022Reward Systems for Trustworthy Medical Federated Learningcited by 6
  4. 2024FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesiscited by 6
  5. 2023FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillationcited by 5
  6. 2024Empowering Federated Learning for Massive Models with NVIDIA FLAREcited by 5
  7. 2024Federated brain tumor segmentation: an extensive benchmarkcited by 5
  8. 2024Navigating Distribution Shifts in Medical Image Analysis: A Surveycited by 5
  9. 2023Scale Federated Learning for Label Set Mismatch in Medical Image Classificationcited by 3
  10. 2024Bridging Data Islands: Geographic Heterogeneity-Aware Federated Learning for Collaborative Remote Sensing Semantic Segmentationcited by 3
  11. 2025Federated Client-tailored Adapter for Medical Image Segmentationcited by 3
  12. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  13. 2022Mutual Information Learned Classifiers: an Information-theoretic Viewpoint of Training Deep Learning Classification Systemscited by 2
  14. 2023Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentationcited by 2
  15. 2024FedCVD: The First Real-World Federated Learning Benchmark on Cardiovascular Disease Datacited by 2
  16. 2025FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysiscited by 2
  17. 2026Conformalized Neural Networks for Federated Uncertainty Quantification under Dual Heterogeneitycited by 2
  18. 2026ProtegoFed: Backdoor-Free Federated Instruction Tuning with Interspersed Poisoned Datacited by 2
  19. 2023Whole-brain radiomics for clustered federated personalization in brain tumor segmentationcited by 1
  20. 2025Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentationcited by 1
  21. 2022Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucinationcited by 0
  22. 2022Mutual Information Learned Classifiers: an Information-theoretic Viewpoint of Training Deep Learning Classification Systemscited by 0
  23. 2024Anatomical 3D Style Transfer Enabling Efficient Federated Learning with Extremely Low Communication Costscited by 0
  24. 2024Federated Black-Box Adaptation for Semantic Segmentationcited by 0
  25. 2025FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentationcited by 0
  26. 2025Asynchronous Federated Continual Segmentation with Evolving Clients and Label Spacescited by 0
  27. 2025UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Datacited by 0
  28. 2025Improving Learning of New Diseases through Knowledge-Enhanced Initialization for Federated Adapter Tuningcited by 0
  29. 2025Privacy-Aware Federated nnU-Net for ECG Page Digitizationcited by 0
  30. 2025Robust Client-Server Watermarking for Split Federated Learningcited by 0