FENDA-FL: Personalized Federated Learning on Heterogeneous Clinical Datasets

Federated learning (FL) is increasingly being recognized as a key approach to overcoming the data silos that so frequently obstruct the training and deployment of machine-learning models in clinical settings. This work contributes to a growing body of FL research specifically focused on clinical applications along three important directions. First, we expand the FLamby benchmark (du Terrail et al., 2022a) to include evaluation of personalized FL methods and demonstrate substantive performance improvements over the original results. Next, we advocate for …

applications clinical cs.lg data datasets data silos deployment expand federated learning key machine personalized research training work