id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-327784-xet20fcw	Rieke, Nicola	The future of digital health with federated learning	2020-09-14		.txt	text/plain	5658	273	42	We envision a federated future for digital health and with this perspective paper, we share our consensus view with the aim of providing context and detail for the community regarding the benefits and impact of FL for medical applications (section "Datadriven medicine requires federated efforts"), as well as highlighting key considerations and challenges of implementing FL for digital health (section "Technical considerations"). FL addresses this issue by enabling collaborative learning without centralising data (subsection "The promise of federated efforts") and has already found its way to digital health applications (subsection "Current FL efforts for digital health"). Current FL efforts for digital health Since FL is a general learning paradigm that removes the data pooling requirement for AI model development, the application range of FL spans the whole of AI for healthcare. Multi-institutional deep learning modeling without sharing patient data: a feasibility study on brain tumor segmentation	./cache/cord-327784-xet20fcw.txt	./txt/cord-327784-xet20fcw.txt