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FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentation

arXiv 2503.15390 ↗First posted 19 Mar 2025eess.IV cs.CVcited by 0

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Abstract (arXiv)

Transformer-based foundation models (FMs) have recently demonstrated remarkable performance in medical image segmentation. However, scaling these models is challenging due to the limited size of medical image datasets within isolated hospitals, where data centralization is restricted due to privacy concerns. These constraints, combined with the data-intensive nature of FMs, hinder their broader application. Integrating federated learning (FL) with foundation models (FLFM) fine-tuning offers a potential solution to these challenges by enabling collaborative model training without data sharing, thus allowing FMs to take advantage of a diverse pool of sensitive medical image data across hospitals/clients. However, non-independent and identically distributed (non-IID) data among clients, paired with computational and communication constraints in federated environments, presents an additional challenge that limits further performance improvements and remains inadequately addressed in existing studies. In this work, we propose a novel FLFM fine-tuning framework, underlineFederated tuning with underlineSimilarity-guided underlineCollaborative underlineAggregation (FedSCA), encompassing all phases of the FL process. This includes (1) specially designed parameter-efficient fine-tuning (PEFT) for local client training to enhance computational efficiency; (2) partial low-level adapter transmission for communication efficiency; and (3) similarity-guided collaborative aggregation (SGCA) on the server side to address non-IID issues. Extensive experiments on three FL benchmarks for medical image segmentation demonstrate the effectiveness of our proposed FedSCA, establishing new SOTA performance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 41 of its 49 references that are in the map. First posted on arXiv 19 Mar 2025.

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References · 41

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2023Segment Anythingcited by 8,234
  5. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  6. 2020End-to-End Object Detection with Transformerscited by 7,414
  7. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  8. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,236
  9. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  10. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  11. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,558
  12. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
29 more
  1. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,222
  2. 2020AdapterFusion: Non-Destructive Task Composition for Transfer Learningcited by 788
  3. 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 641
  4. 2020The Future of Digital Health with Federated Learningcited by 633
  5. 2023Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentationcited by 490
  6. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  7. 2017Road Extraction by Deep Residual U-Netcited by 333
  8. 2020UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentationcited by 332
  9. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 287
  10. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
  11. 2017A Rewriting System for Convex Optimization Problemscited by 179
  12. 2023FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learningcited by 149
  13. 2023MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformercited by 124
  14. 2022FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learningcited by 91
  15. 2023When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directionscited by 84
  16. 2024Selective Aggregation for Low-Rank Adaptation in Federated Learningcited by 80
  17. 2022Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentationcited by 42
  18. 2023Eliminating Domain Bias for Federated Learning in Representation Spacecited by 38
  19. 2021Scaling Federated Learning for Fine-tuning of Large Language Modelscited by 36
  20. 2021Specificity-Preserving Federated Learning for MR Image Reconstructioncited by 35
  21. 2021End-to-End Speech Recognition from Federated Acoustic Modelscited by 30
  22. 2024Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversitycited by 30
  23. 2022Personalizing Federated Medical Image Segmentation via Local Calibrationcited by 29
  24. 2023Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shiftscited by 28
  25. 2024FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuningcited by 27
  26. 2024FedFMS: Exploring Federated Foundation Models for Medical Image Segmentationcited by 21
  27. 2023FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clientscited by 19
  28. 2022Federated Self-supervised Learning for Video Understandingcited by 13
  29. 2024Prototype Correlation Matching and Class-Relation Reasoning for Few-Shot Medical Image Segmentationcited by 13

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