Is Federated Learning Ready for Business? A SWOT Perspective
Müller K (2025)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2025
Event location: Nashville, Tennessee
URI: https://aisel.aisnet.org/icis2025/impl_adopt/impl_adopt/5/
Abstract
Federated learning (FL) provides organizations with a novel solution for developing artificial intelligence (AI) models despite limited centralized data and computing resources. This training approach requires organizations to collaborate with other data owners and pool their knowledge by aggregating local AI models, while keeping sensitive raw data decentralized. Organizations will adopt FL and take on the associated implementation effort if they expect it to advance their AI activities and lead to measurable business value. So far, little is known about how organizations in traditional industries perceive the potential of FL. Therefore, this paper conducts an interview study to identify the adoption factors that decision makers associate with FL. These factors are categorized as strengths, weaknesses, opportunities, and threats of the technology and subsequently rated by companies specializing in FL in terms of their importance and difficulty for a successful endeavor. Insights from both studies advance technology assessment and FL commercialization.
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How to cite
APA:
Müller, K. (2025). Is Federated Learning Ready for Business? A SWOT Perspective. In Proceedings of the International Conference on Information Systems. Nashville, Tennessee, US.
MLA:
Müller, Kristina. "Is Federated Learning Ready for Business? A SWOT Perspective." Proceedings of the International Conference on Information Systems, Nashville, Tennessee 2025.
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