Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
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.
Recommended Citation
Müller, Tobias; Zahn, Milena; and Matthes, Florian, "Revealing the Impacting Factors for the Adoption of Federated Machine Learning in Organizations" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 3.
https://aisel.aisnet.org/hicss-57/st/emerging_technologies/3
Revealing the Impacting Factors for the Adoption of Federated Machine Learning in Organizations
Hilton Hawaiian Village, Honolulu, Hawaii
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.
https://aisel.aisnet.org/hicss-57/st/emerging_technologies/3