Federated learning as a smart tool for research on infectious diseases.

ABSTRACT:

Background

The use of real-world data has become increasingly popular, also in the field of infectious disease (ID), particularly since the COVID-19 pandemic emerged. While much useful data for research is being collected, these data are generally stored across different sources. Privacy concerns limit the possibility to store the data centrally, thereby also limiting the possibility of fully leveraging the potential power of combined data. Federated learning (FL) has been suggested to overcome privacy issues by making it possible to perform research on data from various sources without those data leaving local servers. In this review, we discuss existing applications of FL in ID research, as well as the most relevant opportunities and challenges of this method.

Methods

Refe

SUBMITTER: Zwiers LC 

PROVIDER: S-EPMC11580691 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature