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Multi-level Personalised Federated Learning: A concept for data sovereign machine learning in digital agriculture

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dc.contributor.authorHussaini, Mortesa
dc.contributor.authorStein, Anthony
dc.contributor.editorKlein, Maike
dc.contributor.editorKrupka, Daniel
dc.contributor.editorWinter, Cornelia
dc.contributor.editorGergeleit, Martin
dc.contributor.editorMartin, Ludger
dc.date.accessioned2024-10-21T18:24:12Z
dc.date.available2024-10-21T18:24:12Z
dc.date.issued2024
dc.description.abstractSecuring food supply and protecting the environment at the same time are two of the major challenges modern agriculture is facing. Research into Artificial Intelligence has seen an enormous increase in interest and potential fields of application. In agriculture, farmers can benefit enormously from this technology. However, the diverse potential is also contrasted by a number of socio-economic concerns and challenges. This aspect needs to be addressed in order to foster a trustworthy use and proliferation of AI applications in socio-technical systems, such as digital agriculture. Federated Learning is a ML paradigm that constitutes a promising solution to this challenge. However, in agricultural use cases exhibiting high degrees of data heterogeneity, client-drift can occur and needs to be addressed. In this concept paper, we propose the advancement of Personalised Federated Learning with multiple levels of sub-federations as a potential solution.en
dc.identifier.doi10.18420/inf2024_112
dc.identifier.isbn978-3-88579-746-3
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45084
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2024
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-352
dc.subjectArtificial Intelligence
dc.subjectClient-Drift
dc.subjectData Sovereignty
dc.subjectDigital Agriculture
dc.subjectDistributed Machine Learning
dc.subjectPersonalised Federated Learning
dc.titleMulti-level Personalised Federated Learning: A concept for data sovereign machine learning in digital agricultureen
dc.typeText/Conference Paper
gi.citation.endPage1277
gi.citation.publisherPlaceBonn
gi.citation.startPage1269
gi.conference.date24.-26. September 2024
gi.conference.locationWiesbaden
gi.conference.sessiontitleKoLaZ-24-Kolloquium Landwirtschaft der Zukunft 2024: Digitale Souveränität in der Landwirtschaft, der Lebensmittelkette und dem ländlichen Raum: Trotz, mit oder durch KI?

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P352 - INFORMATIK 2024 - Lock in or log out? Wie digitale Souveränität gelingt