Federated machine learning in data-protection-compliant research

Alissa Brauneck; Louisa Schmalhorst; Mohammad Mahdi Kazemi Majdabadi; Mohammad Bakhtiari; Uwe Völker; Christina Caroline Saak; Jan Baumbach; Linda Baumbach; Gabriele Buchholtz

doi:10.1038/s42256-022-00601-5

Federated machine learning in data-protection-compliant research

Alissa Brauneck
, Louisa Schmalhorst
, Mohammad Mahdi Kazemi Majdabadi
, Mohammad Bakhtiari
, Uwe Völker
, Christina Caroline Saak
, Jan Baumbach
, Linda Baumbach
, Gabriele Buchholtz

Universität Hamburg
University Medicine Greifswald
Universitätsklinikum Hamburg-Eppendorf

Research output: Contribution to journal › Comment/debate

28 Scopus citations

Original language	English
Pages (from-to)	2-4
Number of pages	3
Journal	Nature Machine Intelligence
Volume	5
Issue number	1
DOIs	https://doi.org/10.1038/s42256-022-00601-5
State	Published - Jan 2023
Externally published	Yes

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10.1038/s42256-022-00601-5

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title = "Federated machine learning in data-protection-compliant research",

author = "Alissa Brauneck and Louisa Schmalhorst and \{Kazemi Majdabadi\}, \{Mohammad Mahdi\} and Mohammad Bakhtiari and Uwe V{\"o}lker and Saak, \{Christina Caroline\} and Jan Baumbach and Linda Baumbach and Gabriele Buchholtz",

note = "Funding Information: Our work was funded by the German Federal Ministry of Education and Research (BMBF; grants 16DTM100A and 16DTM100C). We also received funding from the European Union{\textquoteright}s Horizon 2020 research and innovation programme under grant agreement no. 826078. This publication reflects only the authors{\textquoteright} views, and the European Commission is not responsible for any use that may be made of the information it contains. Funding Information: Our work was funded by the German Federal Ministry of Education and Research (BMBF; grants 16DTM100A and 16DTM100C). We also received funding from the European Union{\textquoteright}s Horizon 2020 research and innovation programme under grant agreement no. 826078. This publication reflects only the authors{\textquoteright} views, and the European Commission is not responsible for any use that may be made of the information it contains. ",

year = "2023",

month = jan,

doi = "10.1038/s42256-022-00601-5",

language = "English",

volume = "5",

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T1 - Federated machine learning in data-protection-compliant research

AU - Brauneck, Alissa

AU - Schmalhorst, Louisa

AU - Kazemi Majdabadi, Mohammad Mahdi

AU - Bakhtiari, Mohammad

AU - Völker, Uwe

AU - Saak, Christina Caroline

AU - Baumbach, Jan

AU - Baumbach, Linda

AU - Buchholtz, Gabriele

N1 - Funding Information: Our work was funded by the German Federal Ministry of Education and Research (BMBF; grants 16DTM100A and 16DTM100C). We also received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 826078. This publication reflects only the authors’ views, and the European Commission is not responsible for any use that may be made of the information it contains. Funding Information: Our work was funded by the German Federal Ministry of Education and Research (BMBF; grants 16DTM100A and 16DTM100C). We also received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 826078. This publication reflects only the authors’ views, and the European Commission is not responsible for any use that may be made of the information it contains.

PY - 2023/1

Y1 - 2023/1

UR - https://www.scopus.com/pages/publications/85146808581

U2 - 10.1038/s42256-022-00601-5

DO - 10.1038/s42256-022-00601-5

M3 - Comment/debate

AN - SCOPUS:85146808581

SN - 2522-5839

VL - 5

SP - 2

EP - 4

JO - Nature Machine Intelligence

JF - Nature Machine Intelligence

IS - 1

ER -

Federated machine learning in data-protection-compliant research

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