A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data

Training on multiple diverse data sources is critical to ensure unbiased and generalizable AI. In healthcare, data privacy laws prohibit data from being moved outside the country of origin, preventing global medical datasets being centralized for AI training. Data-centric, cross-silo federated learn...

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Detalles Bibliográficos
Autores principales: Nguyen, T. V., Dakka, M. A., Diakiw, S. M., VerMilyea, M. D., Perugini, M., Hall, J. M. M., Perugini, D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9133021/
https://www.ncbi.nlm.nih.gov/pubmed/35614106
http://dx.doi.org/10.1038/s41598-022-12833-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9133021/
https://www.ncbi.nlm.nih.gov/pubmed/35614106
http://dx.doi.org/10.1038/s41598-022-12833-x

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