Felix Sattler
Felix Sattler
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Zitiert von
Zitiert von
Robust and communication-efficient federated learning from non-iid data
F Sattler, S Wiedemann, KR Müller, W Samek
IEEE transactions on neural networks and learning systems 31 (9), 3400-3413, 2019
Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints
F Sattler, KR Müller, W Samek
IEEE transactions on neural networks and learning systems 32 (8), 3710-3722, 2020
Sparse binary compression: Towards distributed deep learning with minimal communication
F Sattler, S Wiedemann, KR Müller, W Samek
2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019
On the byzantine robustness of clustered federated learning
F Sattler, KR Müller, T Wiegand, W Samek
ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020
Fedaux: Leveraging unlabeled auxiliary data in federated learning
F Sattler, T Korjakow, R Rischke, W Samek
IEEE Transactions on Neural Networks and Learning Systems, 2021
Risk estimation of SARS-CoV-2 transmission from bluetooth low energy measurements
F Sattler, J Ma, P Wagner, D Neumann, M Wenzel, R Schäfer, W Samek, ...
NPJ digital medicine 3 (1), 129, 2020
Communication-efficient federated distillation
F Sattler, A Marban, R Rischke, W Samek
arXiv preprint arXiv:2012.00632, 2020
Cfd: Communication-efficient federated distillation via soft-label quantization and delta coding
F Sattler, A Marban, R Rischke, W Samek
IEEE Transactions on Network Science and Engineering 9 (4), 2025-2038, 2021
Deepcabac: Plug & play compression of neural network weights and weight updates
D Neumann, F Sattler, H Kirchhoffer, S Wiedemann, K Müller, H Schwarz, ...
2020 IEEE International Conference on Image Processing (ICIP), 21-25, 2020
Trends and advancements in deep neural network communication
F Sattler, T Wiegand, W Samek
arXiv preprint arXiv:2003.03320, 2020
Reward-based 1-bit compressed federated distillation on blockchain
L Witt, U Zafar, KY Shen, F Sattler, D Li, W Samek
arXiv preprint arXiv:2106.14265, 2021
Clustered federated learning
F Sattler, KR Müller, W Samek
Proceedings of the NeurIPS’19 Workshop on Federated Learning for Data …, 2019
Concepts for federated learning, client classification and training data similarity measurement
W Samek, F Sattler, T Wiegand, KR Müller
US Patent App. 17/526,739, 2022
Embedded 3D reconstruction of dynamic objects in real time for maritime situational awareness pictures
F Sattler, B Carrillo-Perez, S Barnes, K Stebner, M Stephan, G Lux
The Visual Computer, 1-14, 2023
B2G4: A synthetic data pipeline for the integration of Blender models in Geant4 simulation toolkit.
A Bueno, F Sattler, MP Prada, M Stephan, S Barnes
EGU23, 2023
Real-time embedded reconstruction of dynamic objects for a 3D maritime situational awareness picture
F Sattler, S Barnes, BJ Carrillo Perez, K Stebner, M Stephan, G Lux
European Workshop on Maritime Systems Resilience and Security 2022 (MARESEC …, 2022
Concepts for distributed learning of neural networks and/or transmission of parameterization updates therefor
W Samek, S Wiedemann, F Sattler, KR Müller, T Wiegand
US Patent App. 17/096,887, 2021
Towards a robust real time 3D reconstruction system for dynamic objects in static scenes
F Sattler
Westphalian University of Applied Sciences, 2021
Concepts for Efficient, Adaptive and Robust Deep Learning from Distributed Data
F Sattler
PQDT-Global, 2021
On the Edge. Artists in Dialogue with Humboldt University Collections
S Barnes, J Hennig, F Sattler
Humboldt-Universität zu Berlin, 2018
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