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. 2021 Mar 4;33(1):mzab010.
doi: 10.1093/intqhc/mzab010.

The value of federated learning during and post-COVID-19

Feng Qian  1 , Andrew Zhang  2
Affiliations

The value of federated learning during and post-COVID-19

Feng Qian et al. Int J Qual Health Care. .

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: data privacy; federated learning (FL); healthcare applications; machine learning (ML); value.

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