• Distributed Machine Learning in Federated Data Spaces
• Automated Certification of Machine Learning Models
• Privacy-Preserving-Learning (Privacy-by-Design)
• Multi-Agent Reinforcement Learning

2025

• T. Sachweh, H. Kuhlmann, H. Gößling and T. Liebig, Federated Data Spaces as an Enabler for Smart City Services - Logistics Use-Case Example, IEEE European Technology & Engineering Management Summit (IEEE E-TEMS), Brugge, Belgien, 2025.

2024

• T. Sachweh, P. Haritz and T. Liebig, Using Petri Nets as an Integrated Constraint Mechanism for Reinforcement Learning Tasks, IEEE Intelligent Vehicles Symposium, IV 2024, Jeju Island, Republic of Korea: IEEE, 2024, pp. 1686-1692, ISBN: 979-8-3503-4881-1, http://dblp.uni-trier.de/db/conf/ivs/ivs2024.html#SachwehHL24.
• H. Gößling, D. Maecker, T. Pieper, T. Sachweh and C. Heinbach, A Procedure for Conceptualizing and Implementing Spade Agents, Engineering Multi-Agent Systems: 12th International Workshop, EMAS 2024, Auckland, New Zealand, 2024.
• D. Macker, F. Harenbrock, H. Gößling, T. Sachweh and O. Thomas, Synergizing Trust and Autonomy: Gaia-X Enabled Multi-Agent Ecosystems for Advanced Freight Fleet Management, Engineering Multi-Agent Systems: 12th International Workshop, EMAS 2024, Auckland, New Zealand, 2024.
• D. Maecker, H. Gößling and T. Sachweh, Setting up a ROS2-based Multi-Agent System implementing the Contract Net Protocol and IDS Connectors, Engineering Multi-Agent Systems: 12th International Workshop, EMAS 2024, Auckland, New Zealand, 2024.

2023

• T. Sachweh, H. Kuhlmann and T. Liebig, Empowering Data Owners with Homomorphic Encrypted Federated Learning in Decentralized Data Spaces, International Symposium on Location-Based Big Data and GeoAI 2023 (LocBigDataAI 2023), Cape Town, South Africa, ICA Commission on Location Based Services, 2023.
• M. Kremer, L. Pohling, H. Gösling, C. Heinbach, T. Sachweh, S. Gogineni and K. Berger, An Intelligent Arrival Time Prediction Service in a Federated Data Ecosystem: The Minimum Viable Demonstrator of the GAIA-X 4 ROMS Research Project, 2023. Available at SSRN: https://ssrn.com/abstract=4331859 or http://dx.doi.org/10.2139/ssrn.4331859

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.

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

• Best Paper Award, E-TEMS 2025