FairFML: fair federated machine learning with a case study on reducing gender disparities in cardiac arrest… | ScholarDeck

Paper

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

New research · AI in Medicine

npj health systems · 3w

AI / informaticsnpj health systems· Aug 2025

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

Siqi Li, Qiming Wu, Doudou Zhou … Nan Liu

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AI Evaluation & GovernanceAI / informaticsQ2IF 3.0

Fair federated learning cut gender disparities in cardiac arrest outcome prediction models.

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

Siqi Li … Nan Liu

npj health systems · Aug 2025

Background

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.

90%

Results

90%

fairness gaps between men and women shrank sharply, without hurting accuracy

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Conclusion

Fair Federated Machine Learning 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.

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