Using Federated Machine Learning to Overcome the AI Scale Disadvantage

Yannick Bammens; Paul Hünermund

Using Federated Machine Learning to Overcome the AI Scale Disadvantage

Yannick Bammens, Paul Hünermund

Department of Strategy and Innovation

Hasselt University

Research output: Contribution to journal › Journal article › Research › peer-review

Abstract

A promising new approach to training AI models lets companies with small data sets collaborate while safeguarding proprietary information.

Translated title of the contribution	IA para todos, así es como las pymes pueden usar el small-data
Original language	English
Journal	MIT Sloan Management Review
Volume	65
Issue number	1
Pages (from-to)	54-57
Number of pages	4
ISSN	1532-9194
Publication status	Published - Sept 2023

Bibliographical note

Published online: 22 August 2023

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https://sloanreview.mit.edu/article/using-federated-machine-learning-to-overcome-the-ai-scale-disadvantage/Licence: Unspecified
https://mitsloanreview.mx/data-ia-machine-learning/ia-para-todos-asi-es-como-las-pymes-pueden-usar-el-small-data/Licence: Unspecified

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Cite this

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title = "Using Federated Machine Learning to Overcome the AI Scale Disadvantage",

abstract = "A promising new approach to training AI models lets companies with small data sets collaborate while safeguarding proprietary information.",

author = "Yannick Bammens and Paul H{\"u}nermund",

note = "Published online: 22 August 2023",

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journal = "MIT Sloan Management Review",

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