Das Suchergebnis hat sich seit Ihrer Suchanfrage verändert. Eventuell werden Dokumente in anderer Reihenfolge angezeigt.
  • Treffer 6 von 103
Zurück zur Trefferliste

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.zeige mehrzeige weniger

Metadaten exportieren

Weitere Dienste

Suche bei Google Scholar Statistik - Anzahl der Zugriffe auf das Dokument
Metadaten
Verfasserangaben:Bjarne PfitznerORCiD, Nico SteckhanORCiDGND, Bert ArnrichORCiDGND
DOI:https://doi.org/10.1145/3412357
ISSN:1533-5399
ISSN:1557-6051
Titel des übergeordneten Werks (Englisch):ACM transactions on internet technology : TOIT / Association for Computing
Untertitel (Englisch):a systematic literature review
Verlag:Association for Computing Machinery
Verlagsort:New York
Publikationstyp:Wissenschaftlicher Artikel
Sprache:Englisch
Datum der Erstveröffentlichung:01.07.2021
Erscheinungsjahr:2021
Datum der Freischaltung:24.03.2023
Freies Schlagwort / Tag:Federated learning
Band:21
Ausgabe:2
Aufsatznummer:50
Seitenanzahl:31
Erste Seite:1
Letzte Seite:31
Fördernde Institution:Federal Ministry of Education and Research of GermanyFederal Ministry of Education & Research (BMBF) [01IS19066]; HPI Research School on Data Science and Engineering
Organisationseinheiten:An-Institute / Hasso-Plattner-Institut für Digital Engineering gGmbH
DDC-Klassifikation: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
Publikationsweg:Open Access / Hybrid Open-Access
Verstanden ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.