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pFLFE: Cross-silo Personalized Federated Learning via Feature Enhancement on Medical Image Segmentation

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Abstract

In medical image segmentation, personalized cross-silo federated learning (FL) is becoming popular for utilizing varied data across healthcare settings to overcome data scarcity and privacy concerns. However, existing methods often suffer from client drift, leading to inconsistent performance and delayed training. We propose a new framework, Personalized Federated Learning via Feature Enhancement (pFLFE), designed to mitigate these challenges. pFLFE consists of two main stages: feature enhancement and supervised learning. The first stage improves differentiation between foreground and background features, and the second uses these enhanced features for learning from segmentation masks. We also design an alternative training approach that requires fewer communication rounds without compromising segmentation quality, even with limited communication resources. Through experiments on three medical segmentation tasks, we demonstrate that pFLFE outperforms the state-of-the-art methods.

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Acknowledgements

This work was supported by the National Key R&D Program of China under Grant No.2022YFB2703301.

Author information

Authors and Affiliations

  1. School of Software and Microelectronics, Peking University, Beijing, China

    Luyuan Xie, Manqing Lin, Siyuan Liu, ChenMing Xu, Cong Li, Yuejian Fang, Qingni Shen & Zhonghai Wu

  2. National Engineering Research Center for Software Engineering, Peking University, Beijing, 100871, China

    Luyuan Xie, Siyuan Liu, ChenMing Xu, Cong Li, Yuejian Fang, Qingni Shen & Zhonghai Wu

  3. State University of New York at Buffalo, Buffalo, USA

    Tianyu Luan

Authors
  1. Luyuan Xie

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  2. Manqing Lin

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  3. Siyuan Liu

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  4. ChenMing Xu

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  5. Tianyu Luan

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  6. Cong Li

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  7. Yuejian Fang

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  8. Qingni Shen

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  9. Zhonghai Wu

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Corresponding authors

Correspondence toYuejian Fang orQingni Shen.

Editor information

Editors and Affiliations

  1. Children’s National Hospital/George Washington University, Washington, DC, USA

    Marius George Linguraru

  2. The Chinese University of Hong Kong, Hong Kong, China

    Qi Dou

  3. Technical University of Denmark, Kgs Lyngby, Denmark

    Aasa Feragen

  4. Imperial College London, London, UK

    Stamatia Giannarou

  5. Imperial College London, London, UK

    Ben Glocker

  6. Universitat de Barcelona, Barcelona, Spain

    Karim Lekadir

  7. Helmholtz Munich, Technical University of Munich and King’s College London, Munich, Germany

    Julia A. Schnabel

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Xie, L.et al. (2024). pFLFE: Cross-silo Personalized Federated Learning via Feature Enhancement on Medical Image Segmentation. In: Linguraru, M.G.,et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15010. Springer, Cham. https://doi.org/10.1007/978-3-031-72117-5_56

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