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Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops (MICCAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14393))

Abstract

Federated learning (FL) has recently been applied to skin lesion analysis, but the challenges of huge communication requirements and non-independent and identical distributions have not been fully addressed. The former problem arises from model parameter transfer between the server and clients, and the latter problem is due to differences in imaging protocols and operational customs. To reduce communication costs, dataset distillation methods have been adopted to distill thousands of real images into a few synthetic images (1 image per class) in each local client, which are then used to train a global model in the server. However, these methods often overlook the possible inter-client distribution drifts, limiting the performance of the global model. In this paper, we propose a generalizable dataset distillation-based federated learning (GDD-FL) framework to achieve communication-efficient federated skin lesion classification. Our framework includes the generalization dataset distillation (GDD) method, which explicitly models image features of the dataset into an uncertain Gaussian distribution and learns to produce synthetic images with features close to this distribution. The uncertainty in the mean and variance of the distribution enables the synthetic images to obtain diverse semantics and mitigate distribution drifts. Based on the GDD method, we further develop a communication-efficient FL framework that only needs to transmit a few synthesized images once for training a global model. We evaluate our approach on a large skin lesion classification dataset and compare it with existing dataset distillation methods and several powerful baselines. Our results show that our model consistently outperforms them, particularly in comparison to the classical FL method. All resources can be found at https://github.com/jcwang123/GDD-FL.

Y. Tian and J. Wang — Contributed Equally.

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Authors and Affiliations

  1. Department of Computer Science at School of Informatics, Xiamen University, Xiamen, 361005, China

    Yuchen Tian, Jiacheng Wang & Liansheng Wang

  2. Department of Biomedical Engineering and Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 119276, Singapore

    Yueming Jin

Authors
  1. Yuchen Tian
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  2. Jiacheng Wang
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  3. Yueming Jin
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  4. Liansheng Wang
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Corresponding author

Correspondence to Liansheng Wang .

Editor information

Editors and Affiliations

  1. University of Central Arkansas, Conway, AR, USA

    M. Emre Celebi

  2. Amazon Development Center U.S. Inc., Seattle, WA, USA

    Md Sirajus Salekin

  3. Korea University, Seoul, Korea (Republic of)

    Hyunwoo Kim

  4. University Hospital Bonn, Bonn, Germany

    Shadi Albarqouni

  5. Instituto Superior Técnico, Lisboa, Portugal

    Catarina Barata

  6. Memorial Sloan Kettering Cancer Center, New Yrok, NY, USA

    Allan Halpern

  7. Medical University of Vienna, Vienna, Austria

    Philipp Tschandl

  8. Kenko AI, Barcelona, Spain

    Marc Combalia

  9. Google (United States), Palo Alto, CA, USA

    Yuan Liu

  10. National Institutes of Health, Bethesda, MD, USA

    Ghada Zamzmi

  11. Amazon, USA, Cambridge, MA, USA

    Joshua Levy

  12. Amazon (United States), Fairfax, VI, USA

    Huzefa Rangwala

  13. German Cancer Research Center, Germany, Heidelberg, Germany

    Annika Reinke

  14. Amazon (United States), Baltimore, WA, USA

    Diya Wynn

  15. Vanderbilt University, Brentwood, TN, USA

    Bennett Landman

  16. Korea University, Seoul, Korea (Republic of)

    Won-Ki Jeong

  17. Johns Hopkins University, Baltimore, MD, USA

    Yiqing Shen

  18. University of Surrey, Guildford, UK

    Zhongying Deng

  19. University of Pennsylvania, Philadelphia, PA, USA

    Spyridon Bakas

  20. University of British Columbia, Vancouver, Canada

    Xiaoxiao Li

  21. Imperial College London, London, UK

    Chen Qin

  22. Nvidia, Munich, Germany

    Nicola Rieke

  23. Nvidia Corporation, Bethesda, MD, USA

    Holger Roth

  24. NVIDIA Corporation, Santa Clara, CA, USA

    Daguang Xu

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Tian, Y., Wang, J., Jin, Y., Wang, L. (2023). Communication-Efficient Federated Skin Lesion Classification with Generalizable Dataset Distillation. In: Celebi, M.E., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops . MICCAI 2023. Lecture Notes in Computer Science, vol 14393. Springer, Cham. https://doi.org/10.1007/978-3-031-47401-9_2

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  • DOI: https://doi.org/10.1007/978-3-031-47401-9_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47400-2

  • Online ISBN: 978-3-031-47401-9

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