Timon Sachweh
Research Topics
- Distributed Machine Learning in Federated Data Spaces
- Automated Certification of Machine Learning Models
- Privacy-Preserving-Learning (Privacy-by-Design)
- Multi-Agent Reinforcement Learning
Publikationen
2022
- T. Sachweh, D. Boiar and T. Liebig, Distributed LSTM-Learning from Differentially Private Label Proportions, 2022 IEEE International Conference on Data Mining Workshops (ICDMW), Orlando, FL, USA, 2022, pp. 1071-1078, doi: 10.1109/ICDMW58026.2022.00139.
- Kremer, Marco and Pohling, Lucas and Gösling, Henning and Heinbach, Christoph and Sachweh, Timon and Gogineni, Sonika and Berger, Kolja, An Intelligent Arrival Time Prediction Service in a Federated Data Ecosystem: The Minimum Viable Demonstrator of the GAIA-X 4 ROMS Research Project (October 31, 2022). Available at SSRN: https://ssrn.com/abstract=4331859 or http://dx.doi.org/10.2139/ssrn.4331859
2021
- Sachweh, T., Boiar, D., Liebig, T. (2021), Differentially Private Learning from Label Proportions, In: Kamp, M., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021. Communications in Computer and Information Science, vol 1524, Springer, Cham. https://doi.org/10.1007/978-3-030-93736-2_11