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<p>Federated Learning (FL) enables collaborative training across institutions without sharing sensitive data, a solution for privacy-preserving AI in medical imaging. However, hospital deployment remains challenging due to strict data protection regulations, heterogeneous infrastructures, and limited network accessibility behind firewalls. We introduce TheODen, an open-source framework for Federated training on histopathology Whole Slide Imaging (WSI). It requires no open client-side ports, enabling training through firewalls via a secure reverse-proxy architecture. We conducted, to our knowledge, the first nationwide FL study for histopathology segmentation of colorectal cancer across three German university hospitals, using breast and colorectal cancer datasets without opening firewall ports. TheODen achieves robust segmentation, with global average dice scores of 0.764 on BCSS and 0.754 on SemiCOL despite data heterogeneity and network constraints. These findings underline TheODen’s potential to facilitate secure, large-scale collaborations between medical institutions and to accelerate clinical translation of AI models under real-world infrastructure constraints, providing a privacy-preserving-by-design architecture for future collaborations.</p>

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Nationwide federated learning for histopathology: secure deployment across Germany behind firewalls

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月28日

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  • Niklas Babendererde,
  • Nick Lemke,
  • Jonathan Stieber,
  • Moritz Fuchs,
  • Zhilong Weng,
  • Marie-Lisa Eich,
  • Thomas Lingscheidt,
  • Fabian Mairinger,
  • Reinhard Büttner,
  • Yuri Tolkach,
  • Anirban Mukhopadhyay

摘要

Federated Learning (FL) enables collaborative training across institutions without sharing sensitive data, a solution for privacy-preserving AI in medical imaging. However, hospital deployment remains challenging due to strict data protection regulations, heterogeneous infrastructures, and limited network accessibility behind firewalls. We introduce TheODen, an open-source framework for Federated training on histopathology Whole Slide Imaging (WSI). It requires no open client-side ports, enabling training through firewalls via a secure reverse-proxy architecture. We conducted, to our knowledge, the first nationwide FL study for histopathology segmentation of colorectal cancer across three German university hospitals, using breast and colorectal cancer datasets without opening firewall ports. TheODen achieves robust segmentation, with global average dice scores of 0.764 on BCSS and 0.754 on SemiCOL despite data heterogeneity and network constraints. These findings underline TheODen’s potential to facilitate secure, large-scale collaborations between medical institutions and to accelerate clinical translation of AI models under real-world infrastructure constraints, providing a privacy-preserving-by-design architecture for future collaborations.