Heilmann, X., Corbucci, L., and Cerrato, M. (2026). Rashomon Sets and Model Multiplicity in Federated Learning. Author/Publisher URL

Heilmann, X., Althaus, E., Cerrato, M., et al. (2025). N-Parties Private Structure and Parameter Learning for Sum-Product Networks. Author/Publisher URL

Heilmann, X., Corbucci, L., Cerrato, M., Monreale, A. (2025). FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation. Author/Publisher URL

Corbucci, L., Heilmann, X., 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., 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., Monreale, A. (2025). FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation. arXiv preprint arXiv:2506.21095.

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. In R. Salakhutdinov, Z. Kolter, K. A. Heller, et al. (eds.), ICML (Vols. 235, pp. 18155-18173). PMLR / OpenReview.net. Author/Publisher URL