Treleaven, P;
Smietanka, M;
Pithadia, H;
(2022)
Federated Learning: The Pioneering Distributed Machine Learning and Privacy-Preserving Data Technology.
Computer
, 55
(4)
pp. 20-29.
10.1109/MC.2021.3052390.
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Abstract
Federated learning (pioneered by Google) is a new class of machine learning models trained on distributed data sets, and equally important, a key privacy-preserving data technology. The contribution of this article is to place it in perspective to other data science technologies.
| Type: | Article |
|---|---|
| Title: | Federated Learning: The Pioneering Distributed Machine Learning and Privacy-Preserving Data Technology |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/MC.2021.3052390 |
| Publisher version: | https://doi.org/10.1109/MC.2021.3052390 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Technology, Computer Science, Hardware & Architecture, Computer Science, Software Engineering, Computer Science |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10154054 |
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