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Auflistung nach Schlagwort "Digital Agriculture"

1 - 2 von 2
  • Konferenzbeitrag
    Federated Learning in Agriculture: Potential and Challenges
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Hussaini, Mortesa; Stein, Anthony
    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.
  • Konferenzbeitrag
    Multi-level Personalised Federated Learning: A concept for data sovereign machine learning in digital agriculture
    (INFORMATIK 2024, 2024) Hussaini, Mortesa; Stein, Anthony
    Securing 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.