A Survey on Dataset Distillation: Approaches, Applications and Future Directions J Geng, Z Chen, Y Wang, H Woisetschlaeger, S Schimmler, R Mayer, ... Proceedings of the Thirty-Second International Joint Conference on …, 2023 | 11 | 2023 |
Federated Fine-tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly H Woisetschläger, A Isenko, S Wang, R Mayer, HA Jacobsen To appear in the proceedings of the 8th Workshop on Data Management for End …, 2024 | 8 | 2024 |
A comprehensive study on dataset distillation: Performance, privacy, robustness and fairness Z Chen, J Geng, D Zhu, H Woisetschlaeger, Q Li, S Schimmler, R Mayer, ... arXiv preprint arXiv:2305.03355, 2023 | 3 | 2023 |
A Survey on Efficient Federated Learning Methods for Foundation Model Training H Woisetschläger, A Isenko, S Wang, R Mayer, HA Jacobsen To appear in the Proceedings of the Thirty-Third International Joint …, 2024 | 2 | 2024 |
Federated Learning Priorities Under the European Union Artificial Intelligence Act H Woisetschläger, A Erben, B Marino, S Wang, ND Lane, R Mayer, ... arXiv preprint arXiv:2402.05968, 2024 | 1 | 2024 |
FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems H Woisetschläger, A Isenko, R Mayer, HA Jacobsen arXiv preprint arXiv:2306.05172, 2023 | 1 | 2023 |