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Nguyen, T. V., Dakka, M. A., Diakiw, S. M., VerMilyea, M. D., Perugini, M., Hall, J. M. M., Perugini, D. (2022) A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data. Scientific Reports, 12 (1) doi:10.1038/s41598-022-12833-x

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Reference TypeJournal (article/letter/editorial)
TitleA novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data
JournalScientific Reports
AuthorsNguyen, T. V.Author
Dakka, M. A.Author
Diakiw, S. M.Author
VerMilyea, M. D.Author
Perugini, M.Author
Hall, J. M. M.Author
Perugini, D.Author
Year2022 (December)Volume12
Issue1
PublisherSpringer Science and Business Media LLC
DOIdoi:10.1038/s41598-022-12833-xSearch in ResearchGate
Generate Citation Formats
Mindat Ref. ID15218866Long-form Identifiermindat:1:5:15218866:7
GUID0
Full ReferenceNguyen, T. V., Dakka, M. A., Diakiw, S. M., VerMilyea, M. D., Perugini, M., Hall, J. M. M., Perugini, D. (2022) A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data. Scientific Reports, 12 (1) doi:10.1038/s41598-022-12833-x
Plain TextNguyen, T. V., Dakka, M. A., Diakiw, S. M., VerMilyea, M. D., Perugini, M., Hall, J. M. M., Perugini, D. (2022) A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data. Scientific Reports, 12 (1) doi:10.1038/s41598-022-12833-x
In(2022, December) Scientific Reports Vol. 12 (1) Springer Science and Business Media LLC


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