id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-159103-dbgs2ado	Rieke, Nicola	The Future of Digital Health with Federated Learning	2020-03-18		.txt	text/plain	6703	326	46	The medical FL use-case is inherently different from other domains, e.g. in terms of number of participants and data diversity, and while recent surveys investigate the research advances and open questions of FL [14, 11, 15] , we focus on what it actually means for digital health and what is needed to enable it. Transfer Learning, for example, is a well-established approach of model-sharing that makes it possible to tackle problems with deep neural networks that have millions of parameters, despite the lack of extensive, local datasets that are required for training from scratch: a model is first trained on a large dataset and then further optimised on the actual target data. To adopt this approach into a form of collaborative learning in a FL setup with continuous learning from different institutions, the participants can share their model with a peer-to-peer architecture in a "round-robin" or parallel fashion and train in turn on their local data.	./cache/cord-159103-dbgs2ado.txt	./txt/cord-159103-dbgs2ado.txt