Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing

Li, Andy; Markovic, Milan; Edwards, Peter; Leontidis, Georgios

Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing

dc.contributor.author	Li, Andy
dc.contributor.author	Markovic, Milan
dc.contributor.author	Edwards, Peter
dc.contributor.author	Leontidis, Georgios
dc.contributor.institution	University of Aberdeen.Natural & Computing Sciences	en
dc.contributor.institution	University of Aberdeen.Computing Science	en
dc.contributor.institution	University of Aberdeen.Trusted Things and Communities	en
dc.contributor.institution	University of Aberdeen.Vice Principals	en
dc.contributor.institution	University of Aberdeen.Agents at Aberdeen	en
dc.date.accessioned	2023-07-13T15:17:02Z
dc.date.available	2023-07-13T15:17:02Z
dc.date.issued	2023-04-19
dc.description	31 pages, 4 figures, 4 tables	en
dc.format.extent	31
dc.format.extent	691038
dc.identifier	245779293
dc.identifier	af7e4025-d45c-48b7-bab4-30886130227b
dc.identifier.citation	Li, A, Markovic, M, Edwards, P & Leontidis, G 2023 'Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing' ArXiv. https://doi.org/10.48550/arXiv.2304.09876	en
dc.identifier.doi	10.48550/arXiv.2304.09876
dc.identifier.other	ArXiv: http://arxiv.org/abs/2304.09876v1
dc.identifier.other	ORCID: /0000-0002-4527-9186/work/138945181
dc.identifier.other	ORCID: /0000-0001-6671-5568/work/138946592
dc.identifier.uri	https://hdl.handle.net/2164/21211
dc.identifier.url	http://arxiv.org/abs/2304.09876v1	en
dc.language.iso	eng
dc.publisher	ArXiv
dc.subject	SDG 9 - Industry, Innovation, and Infrastructure	en
dc.subject	SDG 13 - Climate Action	en
dc.subject	2040 Data and Artificial Intelligence	en
dc.subject	cs.LG	en
dc.subject	QA76 Computer software	en
dc.subject.lcc	QA76	en
dc.title	Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing	en
dc.type	Preprint	en

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