Exploration of Digital Transformation Paths of Different Enterprises Based on Federal Learning from the Perspective of Business Model

Authors

DOI:

https://doi.org/10.56294/sctconf2024.1150

Keywords:

business model, Federal learning, Digital transformation of enterprises, NG Boost model optimizatio

Abstract

Digital transformation and upgrading have emerged as central theories for integrating digital consumption with enterprise transformation. This shift is essential for enterprises to meet evolving development needs and establish sustainable interest models. However, practical challenges and a lack of experience often hinder successful transformation, raising doubts about whether digital transformation can genuinely enhance performance. The theoretical exploration of this relationship remains in its early stages, with differing scholarly views on how digitalization affects performance. Objective:This paper aims to explore the mechanisms through which digital transformation impacts enterprise performance. It seeks to address the existing gaps in understanding and to investigate the channels through which digital transformation influences performance, utilizing various theoretical perspectives. Methods:To address high communication costs and data heterogeneity in federated learning, this study introduces local update and gradient compression techniques in optimization algorithms. Additionally, it incorporates gradient tracking to manage data heterogeneity. The paper employs a combination of resource-based theory, empowerment theory, and contingency theory, along with empirical analysis and experimental detection methods, to enrich the research content and depth.Results:The experimental results demonstrate that the optimized NG Boost model is effective in examining enterprises undergoing digital transformation from a dynamic capabilities perspective. This approach proves useful for studying how digital transformation leads to performance enhancement. Conclusion:The study confirms that integrating local updates, gradient compression, and gradient tracking into optimization algorithms can address key challenges in federated learning. The findings highlight the significance of digital transformation in improving enterprise performance, emphasizing the value of dynamic capabilities in achieving performance upgrades.

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Published

2024-08-15

How to Cite

1.
Gong Y. Exploration of Digital Transformation Paths of Different Enterprises Based on Federal Learning from the Perspective of Business Model. Salud, Ciencia y Tecnología - Serie de Conferencias [Internet]. 2024 Aug. 15 [cited 2024 Nov. 8];3:.1150. Available from: https://conferencias.ageditor.ar/index.php/sctconf/article/view/1154