id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-035388-n9hza6vm	Xu, Jie	Federated Learning for Healthcare Informatics	2020-11-12		.txt	text/plain	6143	352	43	This creates a big barrier for developing effective analytical approaches that are generalizable, which need diverse, "big data." Federated learning, a mechanism of training a shared global model with a central server while keeping all the sensitive data in local institutions where the data belong, provides great promise to connect the fragmented healthcare data sources with privacy-preservation. For both provider (e.g., building a model for predicting the hospital readmission risk with patient Electronic Health Records (EHR) [71] ) and consumer (patient)-based applications (e.g., screening atrial fibrillation with electrocardiograms captured by smartwatch [79] ), the sensitive patient data can stay either in local institutions or with individual consumers without going out during the federated model learning process, which effectively protects the patient privacy. Federated learning is a problem of training a high-quality shared global model with a central server from decentralized data scattered among large number of different clients (Fig. 1) .	./cache/cord-035388-n9hza6vm.txt	./txt/cord-035388-n9hza6vm.txt