Towards trust-based data weighting in machine learning

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dc.contributor.authorMurphy, Sean Óg
dc.contributor.authorRoedig, Utz
dc.contributor.authorSreenan, Cormac J.
dc.contributor.authorKhalid, Ahmed
dc.contributor.funderEuropean Science Foundation
dc.contributor.funderScience Foundation Ireland
dc.contributor.funderHorizon 2020
dc.date.accessioned2023-11-10T15:01:28Z
dc.date.available2023-11-08T23:20:25Zen
dc.date.available2023-11-10T15:01:28Z
dc.date.issued2023-10en
dc.date.updated2023-11-08T23:20:26Zen
dc.description.abstractIn distributed environments, data for Machine Learning (ML) applications may be generated from numerous sources and devices, and traverse a cloud-edge continuum via a variety of protocols, using multiple security schemes and equipment types. While ML models typically benefit from using large training sets, not all data can be equally trusted. In this work, we examine data trust as a factor in creating ML models, and explore an approach using annotated trust metadata to contribute to data weighting in generating ML models. We assess the feasibility of this approach using well-known datasets for both linear regression and classification problems, demonstrating the benefit of including trust as a factor when using heterogeneous datasets. We discuss the potential benefits of this approach, and the opportunity it presents for improved data utilisation and processing.
dc.description.sponsorshipScience Foundation Ireland (13/RC/2077 P2)
dc.description.statusPeer revieweden
dc.description.versionAccepted Version
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMurphy, S. Ó., Roedig, U., Sreenan, C. J. and Khalid, A. (2023) 'Towards trust-based data weighting in machine learning' Cloud Edge Continuum Workshop 2023 (CEC23), Reykjavik, Iceland, 10 October.
dc.identifier.endpage6
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/10468/15220
dc.language.isoenen
dc.relation.ispartofCloud Edge Continuum Workshop 2023 (CEC23), Reykjavik, Iceland, 10 October.
dc.relation.projectinfo:eu-repo/grantAgreement/EC/HE::HORIZON-AG/101097560/EU/Collaborative edge-cLoud continuum and Embedded AI for a Visionary industry of thE futuRe/CLEVER
dc.rights© 2023, the Authors.
dc.subjectEdge computing
dc.subjectMachine learning
dc.subjectData confidence fabric
dc.subjectLinear regression
dc.subjectClustering
dc.subjectData weighting
dc.titleTowards trust-based data weighting in machine learning
dc.typeConference item
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