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<p>Health equity is a critical concern in clinical research and practice, as biased predictive models can exacerbate disparities in clinical decision-making and patient outcomes. As healthcare systems increasingly rely on data-driven models, ensuring fairness in these systems is essential to prevent perpetuating existing disparities. While large-scale healthcare data exists across multiple institutions, cross-institutional collaborations often face privacy constraints, highlighting the need for privacy-preserving solutions that also promote fairness. We present Fair Federated Machine Learning (FairFML), a model-agnostic solution designed to reduce algorithmic bias in cross-institutional healthcare collaborations while preserving patient privacy. Validated through a real-world case study on reducing gender disparities in cardiac arrest outcome prediction, FairFML improved fairness metrics by up to 90% without compromising predictive performance. FairFML is flexible and compatible with various FL frameworks and models, from traditional statistical methods to deep learning, offering a robust and scalable solution for equitable model development in clinical settings.</p>

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FairFML: fair federated machine learning with a case study on reducing gender disparities in cardiac arrest outcome prediction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月12日

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  • Siqi Li,
  • Qiming Wu,
  • Doudou Zhou,
  • Xin Li,
  • Di Miao,
  • Chuan Hong,
  • Wenjun Gu,
  • Yuqing Shang,
  • Yohei Okada,
  • Michael Hao Chen,
  • Mengying Yan,
  • Yilin Ning,
  • Marcus Eng Hock Ong,
  • Nan Liu

摘要

Health equity is a critical concern in clinical research and practice, as biased predictive models can exacerbate disparities in clinical decision-making and patient outcomes. As healthcare systems increasingly rely on data-driven models, ensuring fairness in these systems is essential to prevent perpetuating existing disparities. While large-scale healthcare data exists across multiple institutions, cross-institutional collaborations often face privacy constraints, highlighting the need for privacy-preserving solutions that also promote fairness. We present Fair Federated Machine Learning (FairFML), a model-agnostic solution designed to reduce algorithmic bias in cross-institutional healthcare collaborations while preserving patient privacy. Validated through a real-world case study on reducing gender disparities in cardiac arrest outcome prediction, FairFML improved fairness metrics by up to 90% without compromising predictive performance. FairFML is flexible and compatible with various FL frameworks and models, from traditional statistical methods to deep learning, offering a robust and scalable solution for equitable model development in clinical settings.