FairFML: A Unified Approach to Algorithmic Fair Federated Learning with Applications to Reducing Gender Disparities in Cardiac Arrest Outcomes.

Siqi Li¹, Qiming Wu¹, Xin Li¹, Di Miao¹, Chuan Hong², Wenjun Gu¹, Yilin Ning¹, Yuqing Shang¹, Nan Liu¹

¹Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.

²Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.

Abstract

Addressing algorithmic bias in healthcare is crucial for ensuring equity in patient outcomes, particularly in cross-institutional collaborations where privacy constraints often limit data sharing. Federated learning (FL) offers a solution by enabling institutions to collaboratively train models without sharing sensitive data, but challenges related to fairness remain. To tackle this, we propose Fa…

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