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The value of federated learning during and post-COVID-19.
Qian, Feng; Zhang, Andrew.
  • Qian F; Health Policy, Management, and Behavior, School of Public Health, University at Albany-State University of New York, Albany, Room 169, 1 University Place, Rensselaer, NY 12222, USA.
  • Zhang A; Amazon Web Service, 450 West 33rd Street, New York, NY 10001, USA.
Int J Qual Health Care ; 33(1)2021 Mar 04.
Article in English | MEDLINE | ID: covidwho-1066349
ABSTRACT
Federated learning (FL) as a distributed machine learning (ML) technique has lately attracted increasing attention of healthcare stakeholders as FL is perceived as a promising decentralized approach to address data privacy and security concerns. The FL approach stores and maintains the privacy-sensitive data locally while allows multiple sites to train ML models collaboratively. We aim to describe the most recent real-world cases using the FL in both COVID-19 and non-COVID-19 scenarios and also highlight current limitations and practical challenges of FL.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Computer Security / Confidentiality / Electronic Health Records / Machine Learning / COVID-19 Type of study: Observational study / Prognostic study Topics: Long Covid Limits: Humans Language: English Journal subject: Health Services Year: 2021 Document Type: Article Affiliation country: Intqhc

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Computer Security / Confidentiality / Electronic Health Records / Machine Learning / COVID-19 Type of study: Observational study / Prognostic study Topics: Long Covid Limits: Humans Language: English Journal subject: Health Services Year: 2021 Document Type: Article Affiliation country: Intqhc