Ethical AI for Healthcare Systems: Uncertainty-Aware, Fair Federated Learning

This paper proposes U-FARE, an uncertainty-aware fair federated learning (FL) framework aimed at improving disease prediction in healthcare, with a specific focus on Alzheimer's disease detection. U-FARE incorporates evidential neural networks (ENN) to quantify uncertainty, enhancing both model fairness and accuracy. The framework ensures group-level fairness, providing consistent model performance across diverse healthcare environments despite data heterogeneity. We evaluate U-FARE on three real-world healthcare datasets—NACC, OASIS, and ADNI—comparing its performance to several state-of-the-art fairness-aware FL methods. Experimental results demonstrate that U-FARE outperforms baseline methods in both prediction accuracy and fairness, effectively balancing these two crucial aspects. The results also reveal the trade-off between fairness and accuracy, where higher fairness levels may compromise prediction accuracy. U-FARE achieves the highest accuracy (0.928) on the NACC dataset, consistently outperforms the competitive baseline q-FedAvg by 46%, particularly when higher fairness constraints are applied, and outperforms methods like Ditto and q-FFL with minimal accuracy variance and loss disparity. This is the first approach to simultaneously optimize fairness and accuracy in FL for Alzheimer's disease detection, providing a novel solution to the challenge of fair and effective AI in healthcare. The framework demonstrates the potential to address data heterogeneity while ensuring privacy and fairness in real-world applications.