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Federated Learning in Agriculture: Potential and Challenges

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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.editorWohlgemuth, Volker
dc.date.accessioned2023-11-29T14:50:22Z
dc.date.available2023-11-29T14:50:22Z
dc.date.issued2023
dc.description.abstractFederated learning is an emerging technique in machine learning that allows multiple parties to collaboratively train models without sharing raw data. It has been applied in various fields such as healthcare, finance, and transportation. In this paper, we cast light on the potential of federated learning in the highly relevant social-ecological domain of agriculture, a field in which digitization is becoming increasingly prevalent. We briefly introduce the collaborative learning concept of federated learning and briefly consider its postulated benefits and open challenges. The potential of federated learning to overcome concerns against digital technology in agriculture, e.g., data privacy and sovereignty or initial investment and operating costs, is then discussed. We also identify system requirements and stress the necessity of appropriate IT-ecosystems and touch upon specific requirements which can enable federated learning to ensure both a data-sovereign and efficient information and knowledge exchange among multiple parties. Based on that, agricultural use cases where federated learning can unfold its potential by not only improving the quality of machine learning models, but also by alleviating overarching adoption barriers, will be exemplary delineated.de
dc.identifier.doi10.18420/inf2023_170
dc.identifier.isbn978-3-88579-731-9
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43097
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2023 - Designing Futures: Zukünfte gestalten
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-337
dc.subjectFederated Learning
dc.subjectDigital Agriculture
dc.subjectDistributed AI
dc.subjectData Sovereignty
dc.titleFederated Learning in Agriculture: Potential and Challengesde
dc.typeText/Conference Paper
gi.citation.endPage1661
gi.citation.publisherPlaceBonn
gi.citation.startPage1653
gi.conference.date26.-29. September 2023
gi.conference.locationBerlin
gi.conference.sessiontitleÖkologische Nachhaltigkeit - Kolloquium Landwirtschaft der Zukunft - Ist KI ein wesentlicher Schlüssel zur nachhaltigeren Landwirtschaft?

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P337 - INFORMATIK 2023 - Designing Futures: Zukünfte gestalten