In this paper, we therefore want to initiate a discussion on FL's potential, unsolved challenges and related aspects regarding its application to digital ...
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FL has the potential to revolutionize the smart agriculture sector by enabling farmers to train and deploy machine learning models on their own devices, ...
The potential of federated learning to overcome concerns against digital technology in agriculture, e.g., data privacy and sovereignty or initial investment and ...
Jan 1, 2024 · With federated learning, a farmer puts data on a local computer that the algorithm can access rather than sharing the data on a central server.
Dec 2, 2023 · Such production FL systems faces a few challenges [55]: cost communication, heterogeneity, statistical heterogeneity, and privacy concerns. New ...
Nov 6, 2024 · FL has the potential to revolutionize the smart agriculture sector by enabling farmers to train and deploy machine learning models on their ...
Apr 12, 2024 · Despite these challenges, FL offers immense potential for revolutionizing agriculture by empowering farmers with actionable insights while.
Apr 12, 2024 · This paper explores the evolution, applications, and challenges of federated learning, providing a well-rounded understanding of its potential.
This paper applies the FL approach to smart farming, the Federated learning technique is a subset of machine learning that can be regarded as a contribution.
Apr 12, 2024 · This paper explores the application of FL for smart agriculture, examining its potential benefits and implications.