Federated learning as a smart tool for research on infectious diseases
Laura C Zwiers, Diederick E Grobbee, Alicia Uijl, David S Y Ong · 2024
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Description
Laura Zwiers and co-authors review federated learning for infectious disease research in a 2024 paper. Useful health data sit scattered across hospitals and agencies, and privacy rules block pooling them in one place. Federated learning trains models where the data live, sharing only updates.
Thirty studies were sorted into screening, outcome prediction, disease tracking, and vaccine work. Most tackled COVID-19 during the pandemic years. Accuracy matched or beat central methods in the reports surveyed. A catch stands out: most teams simulated the federation by splitting one data set by hand. The authors call for trials on truly scattered real-world data.
Catalog record
| CREATOR | Laura C Zwiers, Diederick E Grobbee, Alicia Uijl, David S Y Ong |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 38 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |