Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/41971
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dc.contributor.authorBAMMENS, Yannick-
dc.contributor.authorHunermund, Paul-
dc.date.accessioned2023-12-18T14:51:04Z-
dc.date.available2023-12-18T14:51:04Z-
dc.date.issued2023-
dc.date.submitted2023-12-18T13:05:53Z-
dc.identifier.citationMIT SLOAN MANAGEMENT REVIEW, 65 (1) , p. 54 -57-
dc.identifier.urihttp://hdl.handle.net/1942/41971-
dc.description.abstractA promising new approach to training AI models lets companies with small data sets collaborate while safeguarding proprietary information.-
dc.language.isoen-
dc.publisherSLOAN MANAGEMENT REVIEW ASSOC, MIT SLOAN SCHOOL MANAGEMENT-
dc.titleUsing Federated Machine Learning to Overcome the AI Scale Disadvantage-
dc.typeJournal Contribution-
dc.identifier.epage57-
dc.identifier.issue1-
dc.identifier.spage54-
dc.identifier.volume65-
local.format.pages4-
local.bibliographicCitation.jcatA1-
dc.description.notesBammens, Y (corresponding author), Hasselt Univ Belgium, Strategy & innovat, Hasselt, Belgium.-
local.publisher.place77 MASSACHUSETTS AVE, E60-100, CAMBRIDGE, MA 02139-4307 USA-
local.type.refereedRefereed-
local.type.specifiedArticle-
dc.identifier.isi001105057400006-
local.provider.typewosris-
local.description.affiliation[Bammens, Yannick] Hasselt Univ Belgium, Strategy & innovat, Hasselt, Belgium.-
local.description.affiliation[Hunermund, Paul] Copenhagen Business Sch Denmark, Strategy & Innovat, Copenhagen, Denmark.-
local.uhasselt.internationalyes-
item.fullcitationBAMMENS, Yannick & Hunermund, Paul (2023) Using Federated Machine Learning to Overcome the AI Scale Disadvantage. In: MIT SLOAN MANAGEMENT REVIEW, 65 (1) , p. 54 -57.-
item.validationecoom 2024-
item.accessRightsOpen Access-
item.fulltextWith Fulltext-
item.contributorBAMMENS, Yannick-
item.contributorHunermund, Paul-
crisitem.journal.issn1532-9194-
crisitem.journal.eissn1532-8937-
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