Federated learning as a smart tool for research on infectious diseases
Open accessAbstract
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: References for this review were identified through searches of MEDLINE/PubMed, Google Scholar, Embase and Scopus until July 2023. We searched for studies using FL in different applications related to ID. RESULTS: Thirty references were included and divided into four sub-topics: disease screening, prediction of clinical outcomes, infection epidemiology, and vaccine research. Most research was related to COVID-19. In all studies, FL achieved good accuracy when predicting diseases and outcomes, also in comparison to non-federated methods. However, most studies did not make use of real-world federated data, but rather showed the potential of FL by using data that was manually partitioned. CONCLUSIONS: FL is a promising methodology which allows using data from several sources, potentially generating stronger and more generalisable results. However, further exploration of FL application possibilities in ID research is needed.
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A federated learning effort among European hospitals predicted COVID-19 development, with each hospital training local models and updates collected to form a global model that accurately predicted critical cases without data sharing.
“A significant case study included the FL effort among European hospitals to predict COVID-19 development. Each hospital trained local models on patient data, and updates were collected to form a global model that accurately predicted critical cases without data sharing [63].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [63]
The Introduction (page not specified) describes local analyses whose model outputs are pooled centrally without sharing patient data. The Results, under “Prediction of clinical outcomes” and “Selection of references” (pages not specified), report federated COVID-19 models that predicted oxygen requirements or mortality and outperformed single-site models. But the cited review does not identify this as an effort among European hospitals: its 20-site outcome-prediction example used sites across the world. It also reports these studies rather than conducting the effort itself. (LLM, conf=0.88)
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- Authors
- Zwiers, L. C., Grobbee, D. E., Uijl, A., & Ong, D. S. Y.
- Affiliations
- Utrecht University (NL)
- Corresponding author
- Zwiers, Laura C.
- Venue
- BMC Infectious Diseases
- Published
- 2024-11-21
- Volume
- 24
- Issue
- 1
- Pages
- 1327-1327
- ISSN
- 1471-2334 (ISSN-L)
- PMC
- PMC11580691
- OpenAlex
- W4404598572
- Cited by
- 24
- References
- 58
- Citations by year
- 2026: 16 · 2025: 7 · 2024: 1
- FWCI
- 0.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Infectious Diseases (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- federated learning, infectious diseases, real-world data, data privacy, COVID-19 pandemic, disease screening, clinical outcome prediction, infectious disease epidemiology, vaccine research
- MeSH terms
- COVID-19, SARS-CoV-2, Communicable Diseases, Humans, Biomedical Research
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