2025

Corbucci, L., Heilmann, X., and Cerrato, M. (2025). Benefits of the Federation? Analyzing the Impact of Fair Federated Learning at the Client Level. Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency, 2232-2248. DOI
Heilmann, X., Corbucci, L., and Cerrato, M. (2025). A Benchmark for Client-level Fairness in Federated Learning. European Workshop on Algorithmic Fairness, 434-438.
Heilmann, X., Corbucci, L., Cerrato, M., and Monreale, A. (2025). FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation. arXiv preprint arXiv:2506.21095.

2024

Heilmann, X., Henkys, V., Apeldoorn, D., et al. (2024). Studying Privacy Aspects of Learned Knowledge Bases in the Context of Synthetic and Medical Data. In Studies in Health Technology and Informatics. IOS Press. DOI
Heilmann, X., Cerrato, M., Althaus, E. (2024). Differentially Private Sum-Product Networks. ICML. Author/Publisher URL