Federated Learning in Agriculture: Potential and Challenges
| dc.contributor.author | Hussaini, Mortesa | |
| dc.contributor.author | Stein, Anthony | |
| dc.contributor.editor | Klein, Maike | |
| dc.contributor.editor | Krupka, Daniel | |
| dc.contributor.editor | Winter, Cornelia | |
| dc.contributor.editor | Wohlgemuth, Volker | |
| dc.date.accessioned | 2023-11-29T14:50:22Z | |
| dc.date.available | 2023-11-29T14:50:22Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Federated 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.doi | 10.18420/inf2023_170 | |
| dc.identifier.isbn | 978-3-88579-731-9 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/43097 | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | INFORMATIK 2023 - Designing Futures: Zukünfte gestalten | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-337 | |
| dc.subject | Federated Learning | |
| dc.subject | Digital Agriculture | |
| dc.subject | Distributed AI | |
| dc.subject | Data Sovereignty | |
| dc.title | Federated Learning in Agriculture: Potential and Challenges | de |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 1661 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 1653 | |
| gi.conference.date | 26.-29. September 2023 | |
| gi.conference.location | Berlin | |
| gi.conference.sessiontitle | Ökologische Nachhaltigkeit - Kolloquium Landwirtschaft der Zukunft - Ist KI ein wesentlicher Schlüssel zur nachhaltigeren Landwirtschaft? |
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