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 Fair Federated Machine Learning (FairFML), a model-agnostic framework designed to reduce algorithmic disparities while preserving patient privacy. Validated in a real-world study on gender disparities in cardiac arrest outcomes, FairFML improved fairness by up to 65% compared to centralized models, without compromising predictive performance.
Li et al. (Thu,) studied this question.