Abstract
Building unbiased and robust machine learning models using datasets from multiple healthcare systems is critical for addressing the needs of diverse patient populations. However, variations in patient demographics and healthcare protocols across systems often lead to significant differences in data distributions. Not Independent and Not Identically Distributed (non-IID) data presents a major challenge in developing effective federated learning (FL) frameworks. This study proposes a method to estimate the non-IID degree between datasets and introduces three metrics (variability, separability, and computational time) to evaluate and compare the performance of non-IID degree estimation methods. We developed a novel non-IID FL algorithm that incorporates the proposed non-IID degree estimation index as regularization into existing FL algorithms for acute kidney injury risk (AKI) prediction. Our results demonstrate that the proposed method for estimating non-IID degree outperforms previous approaches by effectively identifying differences in data distributions between datasets, consistently producing similar estimates of non-IID degree when evaluating different subsamples from the same dataset, requiring significantly less computational time, and providing better interpretability. Finally, we showed that the proposed non-IID FL algorithm achieves higher test accuracy than local learning, concurrent FL algorithms, and centralized learning for the AKI prediction task.