Nawel Benarba

PhD Student
INSA Lyon - LIRIS laboratory

Nawel Benarba received her Ph.D. in computer science from INSA Lyon, France, where she conducted her research at the LIRIS laboratory within the DRIM research group, under the supervision of Prof Sara Bouchenak. Her research interests lie in trustworthy artificial intelligence, with a particular focus on federated learning and privacy-preserving machine learning. She investigates privacy risks in this paradigm and proposes methods to enhance privacy while preserving overall effectiveness. She also explores fairness-related challenges in these settings. Her work aims to develop AI systems that are simultaneously privacy-preserving, fair, and high-performing.

Research Interests

  • Fair, private, and trustworthy AI.
  • Resilient and highly available distributed systems.
  • Ethical, societal, and regulatory implications of AI and distributed technologies.
CV

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).


Academic and Professional Involvement

Collaborations

Regional collaboration with Prof. Angela Bonifati from Lyon 1 University.
National collaboration with Prof. Vania Marangozova from Grenoble Alpes University.
International collaboration with Prof. Pascal Felber and Valerio Schiavoni from Neuchatel University in Switzerland. And with Prof. Roberto Esposito and Dr. Mirko Polato from Torino University in Italy.

Teaching

Network Programming
INSA Lyon - 2023
Introduction to network computer systems.

Introductory Mathematics Courses
INSA Lyon - 2024
Fundamental mathematics support program for international students.

Paper review

Supervision and scientific mediation

Co-supervision of Master’s Students

INSA Lyon - 2025

Mathieu Chevalier, James Sudlow, and Artur Vieira Pereira, three fifth-year students, working on data science and machine learning.

INSA Lyon - 2024

Thanh Lam Dang, an exchange fifth-year students, working on data management and bias.

Mathieu Chevalier, James Sudlow, and Artur Vieira Pereira, three fourth-year interns, working on identifying bias-inducing attributes during their internship.

Zeyang KONG, a fourth-year intern working on the impact of data management on bias.

Science Engagement Initiatives for Youth
LIRIS - 2023

In March, I presented my doctoral studies and research topic to a middle school intern, and in December, I shared these insights with two groups of three middle school students.

Collective responsibilities

Doctoral Student Representative

Doctoral School InfoMaths Lyon – 2023, Present

  • Member of the doctoral school’s administrative board.
  • Act as a link between PhD students and the doctoral school.
  • Organization of the doctoral school’s scientific day.


DRIM Team Seminar Coordinator

LIRIS Lyon – 2023, 2024

  • Seminars for PhD students to showcase their research.
  • Discussion sessions with presentations for reading groups.


Student Volunteer


Talks

  • Paper presentation at the DSN 2025 Conference, “Anomaly Detection in Energy Performance Certificates – From Oblivious to Enlightened,” June 2025.
  • Introductory session on Federated Learning coding for the CITADEL project, May 2025.
  • Paper and poster presentation at the Middleware 2024 Conference, “Personalized Privacy-Preserving Federated Learning,” December 2024.
  • Paper presentation at the UbiComp 2024 Conference, “Bias Mitigation in Federated Learning for Edge Computing,” October 2024.
  • Invited talk at the ComPAS 2024 Conference, “ASTRAL: Accurate biaS miTigation in fedeRAted Learning,” Nantes, France, July 2024.
  • Invited talk at the M4DI National Research Project Meeting, “Bias in Federated Learning,” March 2024.
  • Paper presentation at the ComPAS 2023 Conference, “Empirical Analysis of Bias in Federated Learning,” July 2023.
  • Paper presentation at the IRIXYS Workshop, “ASTRAL: Accurate biaS miTigation in fedeRAted Learning,” May 2023.
  • Paper presentation at the CNRS GDR-RSD Non-Thematic Days, “Fairness and Privacy Issues in Federated Learning,” January 2023.