Federated Learning for multi-omics: a performance evaluation in Parkinson's disease

Abstract

While machine learning (ML) research has recently grown more in popularity, its application in the omics domain is constrained by access to sufficiently large, high-quality datasets needed to train ML models. Federated Learning (FL) represents an opportunity to enable collaborative curation of such datasets among participating institutions. We compare the simulated performance of several models trained using FL against classically trained ML models on the task of multi-omics Parkinsons Disease prediction. We find that FL model performance tracks centrally trained ML models, where the most performant FL model achieves an AUC-PR of 0.876 {+/-} 0.009, 0.014 {+/-} 0.003 less than its centrally trained variation. We also determine that the dispersion of samples within a federation plays a meaningful role in model performance. Our study implements several open source FL frameworks and aims to highlight some of the challenges and opportunities when applying these collaborative methods in multi-omics studies. The Bigger PictureThe wide-scale application of artificial intelligence and computationally intensive analytical approaches in the biomedical and clinical domain is largely restricted by access to sufficient training data. This data scarcity exists due to the isolated nature of biomedical and clinical institutions, mandated by patient privacy policies in the health system or government legislation. Federated Learning (FL), a machine learning approach that facilitates collaborative model training is a promising strategy to address these restrictions. Therefore, understanding the limitations of cooperatively trained FL models, and their performance differences to similar, centrally trained models, is crucial to valuing their implementation in the broader biomedical research community.