The search result changed since you submitted your search request. Documents might be displayed in a different sort order.
  • search hit 1 of 764
Back to Result List

Federated learning in a medical context

  • Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients' anonymity. However, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area, and many domains have been identified for the application of those methods. This systematic literature reviewData privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients' anonymity. However, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area, and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.show moreshow less

Export metadata

Additional Services

Search Google Scholar Statistics
Metadaten
Author details:Bjarne PfitznerORCiD, Nico SteckhanORCiDGND, Bert ArnrichORCiDGND
DOI:https://doi.org/10.1145/3412357
ISSN:1533-5399
ISSN:1557-6051
Title of parent work (English):ACM transactions on internet technology : TOIT / Association for Computing
Subtitle (English):a systematic literature review
Publisher:Association for Computing Machinery
Place of publishing:New York
Publication type:Article
Language:English
Date of first publication:2021/07/01
Publication year:2021
Release date:2023/03/24
Tag:Federated learning
Volume:21
Issue:2
Article number:50
Number of pages:31
First page:1
Last Page:31
Funding institution:Federal Ministry of Education and Research of GermanyFederal Ministry of Education & Research (BMBF) [01IS19066]; HPI Research School on Data Science and Engineering
Organizational units:An-Institute / Hasso-Plattner-Institut für Digital Engineering gGmbH
DDC classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften / 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Peer review:Referiert
Publishing method:Open Access / Hybrid Open-Access
Accept ✔
This website uses technically necessary session cookies. By continuing to use the website, you agree to this. You can find our privacy policy here.