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Federated machine learning in data-protection-compliant research

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

in 706/134 / 706/648/280 / Access control / Accountability / Artificial intelligence / Big Data / Certification / Collaboration / Comment / Compliance / Data integrity / Datasets / Documentation / Engineering / General Data Protection Regulation / Machine learning / Network security / Personal information / Principles / Privacy / Researchers / Security management / Transparency / Trust

2023

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Federated machine learning in data-protection-compliant research

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

2023

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Federated machine learning in data-protection-compliant research

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

2023

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Journal Article

Federated machine learning in data-protection-compliant research

Baumbach, Linda,

Saak, Christina Caroline,

Kazemi Majdabadi, Mohammad Mahdi,

Schmalhorst, Louisa,

Buchholtz, Gabriele,

Baumbach, Jan,

Brauneck, Alissa,

Bakhtiari, Mohammad,

Völker, Uwe

2023

Overview

To fully leverage big data, they need to be shared across institutions in a manner compliant with privacy considerations and the EU General Data Protection Regulation (GDPR). Federated machine learning is a promising option.

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Publisher

Nature Publishing Group UK,Nature Publishing Group

Subject

/ Artificial intelligence

/ Big Data

/ Certification