University of Aberdeen logo

AURA - Aberdeen University Research Archive

 

Model pruning enables localized and efficient federated learning for yield forecasting and data sharing

Simple item page
dc.contributor.authorLi, Andy Hanou
dc.contributor.authorMarkovic, Milan
dc.contributor.authorEdwards, Pete
dc.contributor.authorLeontidis, Georgios
dc.contributor.institutionUniversity of Aberdeen.Natural & Computing Sciencesen
dc.contributor.institutionUniversity of Aberdeen.Computing Scienceen
dc.contributor.institutionUniversity of Aberdeen.Agents at Aberdeenen
dc.contributor.institutionUniversity of Aberdeen.Machine Learningen
dc.date.accessioned2023-12-07T20:08:00Z
dc.date.available2023-12-07T20:08:00Z
dc.date.issued2024-05-15
dc.descriptionThe work described here was funded by the EPSRC ‘Enhancing Agri-Food Transparent Sustainability’ (EATS) project, United Kingdom (grant number: EP/V042270/1) and by a University of Aberdeen Ph.D. studentship, United Kingdom. We also thank the University of Aberdeen’s HPC facility Maxwell. Open Access via the Elsevier Agreementen
dc.description.statusPeer revieweden
dc.format.extent12
dc.format.extent1315645
dc.identifier282742138
dc.identifier27572bde-56e8-470c-9cb3-cbd9fca690a3
dc.identifier85180562597
dc.identifier.citationLi, A H, Markovic, M, Edwards, P & Leontidis, G 2024, 'Model pruning enables localized and efficient federated learning for yield forecasting and data sharing', Expert Systems with Applications, vol. 242, 122847, pp. 1-12. https://doi.org/10.1016/j.eswa.2023.122847en
dc.identifier.doi10.1016/j.eswa.2023.122847
dc.identifier.issn0957-4174
dc.identifier.otherORCID: /0000-0002-4527-9186/work/148505193
dc.identifier.otherORCID: /0000-0001-6671-5568/work/148506881
dc.identifier.urihttps://hdl.handle.net/2164/22372
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0957417423033493en
dc.identifier.vol242en
dc.language.isoeng
dc.relation.ispartofExpert Systems with Applicationsen
dc.subjectSubstantive connection via an eligible employment contracten
dc.subjectSDG 13 - Climate Actionen
dc.subjectSDG 2 - Zero Hungeren
dc.subject2040 Data and Artificial Intelligenceen
dc.subjectFederated Learningen
dc.subjectPruningen
dc.subjectDeep Learningen
dc.subjectyield forecastingen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectArtificial Intelligenceen
dc.subject.lccQA75en
dc.titleModel pruning enables localized and efficient federated learning for yield forecasting and data sharingen
dc.typeJournal articleen

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
Leontidis_1-s2.0-S0957417423033493-main.pdf
Size:
1.25 MB
Format:
Adobe Portable Document Format
Download

Collections

All research