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Revealing the Impacting Factors for the Adoption of ...
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Further studies confirm that organizational factors such as top management support and thereby organizational readiness are key factors in the adoption of AI in.
(PDF) Revealing the Impacting Factors for the Adoption of ...
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Specifically, we draw on the Technology-Organization-Environment framework and identified a total of 19 influencing factors. Thereby, we intend to facilitate ...
Revealing the Impacting Factors for the Adoption of ...
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2023年12月5日 — Specifically, we draw on the Technology-Organization-Environment framework and identified a total of 19 influencing factors. Thereby, we intend ...
Milena Zahn
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Revealing the Impacting Factors for the Adoption of Federated Machine Learning in Organizations. T Müller, M Zahn, F Matthes. 2, 2024. A Process Model for the ...
Tobias Müller
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2021. Revealing the Impacting Factors for the Adoption of Federated Machine Learning in Organizations. T Müller, M Zahn, F Matthes. 2, 2024. Solving Large ...
Guiding the Practical Adoption of Federated Machine ...
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由 T Müller 著作2024 — First, we identified the influencing factors for the organizational adoption of FedML. Thereby, we uncovered novel factors specific to FedML and demonstrated ...
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Federated learning: Overview, strategies, applications ...
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由 B Yurdem 著作2024 — Federated learning (FL) is a distributed machine learning process, which allows multiple nodes to work together to train a shared model without exchanging raw ...
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Application, Adoption, and Impact of Emerging Technologies
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... Revealing the Impacting Factors for the Adoption of Federated Machine Learning in Organizations. Tobias Müller, Technical University of Munich Milena Zahn ...
Barriers to the Practical Adoption of Federated Machine ...
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Using federated learning to collaborate with other parties is becoming common when conducting machine learning on high-value data. In our work, we try to expand ...
Fairness and privacy preserving in federated learning
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由 TH Rafi 著作2024被引用 42 次 — These privacy and fairness problems, if not addressed properly, might have a severe impact on the dependability of FL as an alternative to centralized ML ...
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