Project KILANKO – Künstliche Intelligenz in landwirtschaftlichen Kommunikationsnetzen - Föderiertes Lernen zur Optimierung…

Basic data

Künstliche Intelligenz in landwirtschaftlichen Kommunikationsnetzen - Föderiertes Lernen zur Optimierung des Netzwerkmanagements
01/05/2020 to 30/04/2023
Abstract / short description:
Unmanned aerial vehicles (UAVs) have the potential to significantly change the world by supplying various types of services that are complex, expensive, or dangerous to provide otherwise. In particular, UAVs assist with automation in several industrial domains, such as agriculture. In the context of this project, UAVs serve as moving base-stations to enhance wireless connectivity over the agriculture site at different levels, for example, between agricultural machinery and sensors. To optimize control and resource allocation of UAV networks, artificial intelligence, and machine learning are of great importance. The core contribution of this subproject encompasses developing resource-efficient, accurate, and interpretable algorithms for federated- and transfer-learning that can be applied by UAVs to optimize the performance of the agriculture site in different perspectives, and in particular, regarding wireless communications.

Involved staff


Wilhelm Schickard Institute of Computer Science (WSI)
Department of Informatics, Faculty of Science

Local organizational units

Department of Informatics
Faculty of Science
University of Tübingen


Bonn, Nordrhein-Westfalen, Germany

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