| A Survey on Dataset Distillation: Approaches, Applications and Future Directions J Geng, Z Chen, Y Wang, H Woisetschlaeger, S Schimmler, R Mayer, ... IJCAI'23: Proceedings of the Thirty-Second International Joint Conference on …, 2023 | 14 | 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 DEEM '24: Proceedings of the Eighth Workshop on Data Management for End-to …, 2024 | 8 | 2024 |
| A Survey on Efficient Federated Learning Methods for Foundation Model Training H Woisetschläger, A Isenko, S Wang, R Mayer, HA Jacobsen IJCAI'24: Proceedings of the Thirty-Third International Joint Conference on …, 2024 | 6 | 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 | 3 | 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 |
| Fledge: Benchmarking federated machine learning applications in edge computing systems H Woisetschläger, A Isenko, R Mayer, HA Jacobsen arXiv preprint arXiv:2306.05172, 2023 | 2 | 2023 |
| Federated Learning and AI Regulation in the European Union: Who is liable? An Interdisciplinary Analysis H Woisetschläger, S Mertel, C Krönke, R Mayer, HA Jacobsen arXiv preprint arXiv:2407.08105, 2024 | | 2024 |
Ruben MayerUniversity of Bayreuth在 uni-bayreuth.de 的电子邮件经过验证
