Publikationen

Autor	Aichroth, Patrick; Antes, Christoph; Gembaczka, Pierre; Graf, Holger; Johnson, David S.; Jung, Matthias; Kämpfe, Thomas; Kleinberger, Thomas; Köllmer, Thomas; Kuhn, Thomas; Kutter, Christoph; Krüger, Jens; Loroch, Dominik M.; Lukashevich, Hanna; Laleni, Nelli; Zhang, Lei; Leugering, Johannes; Martín Fernández, Rodrigo; Mateu, Loreto; Mojumder, Shaown; Prautsch, Benjamin; Pscheidl, Ferdinand; Roscher, Karsten; Schneickert, Sören; Vanselow, Frank; Wallbott, Paul; Walter, Oliver; Weber, Nico
Datum	2022
Art	Conference Paper
Abstrakt	Due to the slow-down of Moore’s Law and Dennard Scaling, new disruptive computer architectures are mandatory. One such new approach is Neuromorphic Computing, which is inspired by the functionality of the human brain. In this position paper, we present the projected SEC-Learn ecosystem, which combines neuromorphic embedded architectures with Federated Learning in the cloud, and performance with data protection and energy efficiency.
Konferenz	International Conference on Embedded Computer Systems - Architectures, Modeling, and Simulation 2021
ISBN	978-3-031-04580-6
Publisher	Springer
Url	https://publica.fraunhofer.de/handle/publica/417959

SEC-Learn: Sensor Edge Cloud for Federated Learning