SEC-Learn: Sensor Edge Cloud for Federated Learning

AutorAichroth, 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
ArtConference Paper
AbstraktDue 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.
KonferenzInternational Conference on Embedded Computer Systems - Architectures, Modeling, and Si­mu­la­tion 2021