Federated Learning for Predicting Postoperative Remission of Patients with Acromegaly: A Multicentered Study.

Background: Decentralized federated learning (DFL) may serve as a useful framework for machine learning (ML) tasks in multicentered studies, maximizing the use of clinical data without data sharing. We aim to propose the first workflow of DFL for ML tasks in multicentered studies, which can be as powerful as those using centralized data.

Methods: A DFL workflow was developed with 4 steps: registration, local computation, model update, and inspection.