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Federated learning (FL) enables collaborative model training while keeping data locally. Currently, most FL studies in radiology are conducted in simulated environments due to numerous hurdles impeding its translation into practice. The few existing real-world FL initiatives rarely communicate specific measures taken to overcome these hurdles.

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Abstract: Real-world Federated Learning in Radiology

  • 会议论文

  • 发表日期：

    2025年

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  • Markus Bujotzek,
  • Ünal Akünal,
  • Stefan Denner,
  • Peter Neher,
  • Maximilian Zenk,
  • Klaus Maier-Hein,
  • Andreas Bucher,
  • Rickmer Braren

摘要

Federated learning (FL) enables collaborative model training while keeping data locally. Currently, most FL studies in radiology are conducted in simulated environments due to numerous hurdles impeding its translation into practice. The few existing real-world FL initiatives rarely communicate specific measures taken to overcome these hurdles.