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.…
Verfasserangaben: | Bjarne PfitznerORCiD, Nico SteckhanORCiDGND, Bert ArnrichORCiDGND |
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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 |