Record link: 
http://hdl.handle.net/20.500.12708/196499
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Title: 
Towards FAIR Data in Distributed Machine Learning Systems
en
Citation: 
Mou, Y., Guo, F., Lu, W., Li, Y., Beyan, O., Rose, T., Dustdar, S., & Decker, S. (2023). Towards FAIR Data in Distributed Machine Learning Systems. In GLOBECOM 2323 - 2023 IEEE Global Communications Conference (pp. 6450–6455). IEEE. https://doi.org/10.1109/GLOBECOM54140.2023.10437414
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Publisher DOI: 
10.1109/GLOBECOM54140.2023.10437414
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Mou, Yongli 
Guo, Fengyang 
Lu, Wei 
Li, Yongzhao 
Beyan, Oya 
Rose, Thomas 
Dustdar, Schahram 
Decker, Stefan 
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Organisational Unit: 
E194-02 - Forschungsbereich Distributed Systems 
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Published in: 
GLOBECOM 2323 - 2023 IEEE Global Communications Conference 
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ISBN: 
979-8-3503-1090-0
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DOI of the book: 
10.1109/GLOBECOM54140.2023
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Date (published): 
2023
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Event name: 
IEEE Global Communications Conference (GLOBECOM 2023) 
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Event date: 
4-Dec-2023 - 8-Dec-2023
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Event place: 
Kuala Lumpur, Malaysia
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Number of Pages: 
6
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Publisher: 
IEEE
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Keywords: 
distributed machine learning; FAIR data principles; federated learning
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Project (external): 
German Research Foundation DFG project NFDI4Health
German Ministry for Research and Education BMBF project WestAI
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Project ID: 
grant no. 442326535
grant no. 01IS22094D
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Research Areas: 
Information Systems Engineering: 100%
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Science Branch: 
1020 - Informatik: 100%
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Appears in Collections:
Conference Paper

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