no code implementations • 7 Mar 2023 • David Neumann, Andreas Lutz, Karsten Müller, Wojciech Samek
A recent distributed learning scheme called federated learning has made it possible to learn from personal user data without its central collection.
no code implementations • 30 May 2022 • Haley Hoech, Roman Rischke, Karsten Müller, Wojciech Samek
Federated learning suffers in the case of non-iid local datasets, i. e., when the distributions of the clients' data are heterogeneous.
no code implementations • 9 Apr 2022 • Daniel Becking, Heiner Kirchhoffer, Gerhard Tech, Paul Haase, Karsten Müller, Heiko Schwarz, Wojciech Samek
Federated learning (FL) scenarios inherently generate a large communication overhead by frequently transmitting neural network updates between clients and server.
no code implementations • 9 Sep 2021 • Daniel Becking, Maximilian Dreyer, Wojciech Samek, Karsten Müller, Sebastian Lapuschkin
The remarkable success of deep neural networks (DNNs) in various applications is accompanied by a significant increase in network parameters and arithmetic operations.