The value of federated learning during and post-COVID-19.
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.
Keywords
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
Similar
MEDLINE
...
LILACS
LIS