Fairness-Aware Federated Learning Framework for ICU Mortality Prediction

Abstract

Accurate prediction of ICU mortality is important for clinical decision-making and resource allocation. In the Federated Learning (FL) approach, privacy is maintained using decentralized hospital data to train machine learning models. Biases in hospital data may increase health inequalities. To address the issue, a new fairness-aware FL approach is developed. The two main metrics, Equalized Odds and Demographic Parity, are integrated in the FL optimization process. The proposed FL model uses adaptive client weighting, thereby minimizing bias propagation within the model. The approach is tested using the MIMIC-IV database. The experimental results demonstrate that the proposed model reduces subgroup differences without compromising predictive accuracy. Ablation studies prove that the combination of fairness regularization and client weighting gives better results. Statistical analysis validates that the achieved fairness improvement is clinically meaningful and robust. The findings conclude that fairness-aware federated learning for predicting critical care outcomes is a strong and viable framework balancing privacy, accuracy, and fairness. This proves to be an important step in the development of ethical AI in healthcare.