Revealing the Impacting Factors for the Adoption of Federated Machine Learning in Organizations

Simple item page
dc.contributor.author Müller, Tobias
dc.contributor.author Zahn, Milena
dc.contributor.author Matthes, Florian
dc.date.accessioned 2023-12-26T18:53:35Z
dc.date.available 2023-12-26T18:53:35Z
dc.date.issued 2024-01-03
dc.identifier.isbn 978-0-9981331-7-1
dc.identifier.other 217f0591-15da-430f-b4ed-6e2f9366dedb
dc.identifier.uri https://hdl.handle.net/10125/107267
dc.language.iso eng
dc.relation.ispartof Proceedings of the 57th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Application, Adoption, and Impact of Emerging Technologies Minitrack
dc.subject federated machine learning
dc.subject interview study
dc.subject technology adoption
dc.subject toe framework
dc.title Revealing the Impacting Factors for the Adoption of Federated Machine Learning in Organizations
dc.type Conference Paper
dc.type.dcmi Text
dcterms.abstract The success of Machine Learning is driven by the ever-increasing wealth of digitized data. Still, a significant amount of the world’s data is scattered and locked in data silos, which leaves its full potential and therefore economic value largely untapped. Federated Machine Learning is a novel model-to-data approach that enables the training of Machine Learning models on decentralized, potentially siloed data. Despite its potential, most Federated Machine Learning projects fail to actualize. The current literature lacks an understanding of the crucial factors for the adoption of Federated Machine Learning in organizations. We conducted an interview study with 13 experts from seven organizations to close this research gap. Specifically, we draw on the Technology-Organization-Environment framework and identified a total of 19 influencing factors. Thereby, we intend to facilitate managerial decision-making, aid practitioners in avoiding pitfalls, and thereby ease the successful implementation of Federated Machine Learning projects.
dcterms.extent 10 pages
prism.startingpage 7343
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
0717.pdf
Size:
221.3 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Application, Adoption, and Impact of Emerging Technologies