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Auflistung nach Schlagwort "Federated Learning"

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.
  • Textdokument
    Towards Collaborative Predictive Maintenance Leveraging Private Cross-Company Data
    (INFORMATIK 2020, 2021) Mohr, Marisa; Becker, Christian; Möller, Ralf; Richter, Matthias
    The accuracy of a predictive maintenance model is largely determined by the available training data. This puts such machine learning systems out of reach for small and medium-sized production engineering companies, as they are often unable to provide training data in sufficient quality and quantity. Building a collaborative model by pooling training data across many companies would solve this issue, but this data cannot simply be consolidated in a central location while at the same time preserving data integrity and security. This paper enables a collaborative model for predictive maintenance on cross-company data without exposing participants' business information by connecting two recent methodologies: blockchain and federated learning.