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FedContrast-GPA: Heterogeneous Federated Optimization via Local Contrastive Learning and Global Process-Aware Aggregation

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

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14221))

Abstract

Federated learning is a promising strategy for performing privacy-preserving, distributed learning for medical image segmentation. However, the data-level heterogeneity as well as system-level heterogeneity makes it challenging to optimize. In this paper, we propose to improve Federated optimization via local Contrastive learning and Global Process-aware Aggregation (referred as FedContrast-GPA), aiming to jointly address both data-level and system-level heterogeneity issues. In specific, To address data-level heterogeneity, we propose to learn a unified latent feature space via an intra-client and inter-client local prototype based contrastive learning scheme. Among which, intra-client contrastive learning is adopted to improve the discriminative ability of learned feature embedding at each client, while inter-client contrastive learning is introduced to achieve cross-client distribution perception and alignment in a privacy preserving manner. To address system-level heterogeneity, we further propose a simple yet effective process-aware aggregation scheme to achieve effective straggler mitigation. Experimental results on six prostate segmentation datasets demonstrate large performance boost over existing state-of-the-art methods.

This study was partially supported by the Natural Science Foundation of China via project U20A20199 and 62201341.

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Authors and Affiliations

  1. Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, No. 800, Dongchuan Road, Shanghai, 200240, China

    Qin Zhou & Guoyan Zheng

Authors
  1. Qin Zhou
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  2. Guoyan Zheng
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Correspondence to Guoyan Zheng .

Editor information

Editors and Affiliations

  1. Icahn School of Medicine, Mount Sinai, NYC, NY, USA, Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  2. Emory University, Atlanta, GA, USA

    Anant Madabhushi

  3. Queen’s University, Kingston, ON, Canada

    Parvin Mousavi

  4. The University of British Columbia, Vancouver, BC, Canada

    Septimiu Salcudean

  5. Yale University, New Haven, CT, USA

    James Duncan

  6. IBM Research, San Jose, CA, USA

    Tanveer Syeda-Mahmood

  7. Johns Hopkins University, Baltimore, MD, USA

    Russell Taylor

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Zhou, Q., Zheng, G. (2023). FedContrast-GPA: Heterogeneous Federated Optimization via Local Contrastive Learning and Global Process-Aware Aggregation. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14221. Springer, Cham. https://doi.org/10.1007/978-3-031-43895-0_62

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  • DOI: https://doi.org/10.1007/978-3-031-43895-0_62

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43894-3

  • Online ISBN: 978-3-031-43895-0

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