Publications

Benarba, N., & Bouchenak, S. (2025). Bias in Federated Learning: A Comprehensive Survey. ACM Computing Surveys.

Benarba, N., Chevalier, M., Bouchenak, S., Bertin, B., & Jung, O. (2025). Anomaly Detection in Energy Performance Certificates–From Oblivious to Enlightened. In Proceedings of the 55th Annual IEEE/IFIP International Conference on Dependable Systems and Networks – Supplemental Volume (DSN-S 2025).

Boscher, C., Benarba, N., Elhattab, F., & Bouchenak, S. 2024. Personalized Privacy-Preserving Federated Learning. In Proceedings of the 25th International Middleware Conference (Middleware 2024).

Djebrouni, Y., Benarba, N., Touat, O., De Rosa, P., Bouchenak, S., Bonifati, A., Felber, P., Marangozova, V., Schiavoni, V. 2024. Bias Mitigation in Federated Learning for Edge Computing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 7(4).

Open Source Software

ASTRAL: Accurate BiaS MiTigation in FedeRAted Learning
A federated learning bias mitigation system.

DINAR: Fine-graineD Privacy-PreservINg FederAted LeaRning
A privacy-preserving mechanism against membership inference attacks.