Home \ Faculty Details \ Dr. Sawsan AbdulRahman
Faculty Img

  • Phone:

    9236

  • Email:

    sabdulrahman@pmu.edu.sa

  • Office No:

    F116

  • Dr. Sawsan AbdulRahman

  • Job Title :

    Assistant Professor

  • College :

    College of Computer Engineering and Science


  • Department :

    Software Engineering


Dr. Sawsan AbdulRahman is an Assistant Professor in the Department of Software Engineering at Prince Mohammad bin Fahd University (PMU), KSA. She received her Ph.D. from École de Technologie Supérieure (ÉTS), Canada, in 2022, her M.S. degree from the Lebanese American University (LAU), Lebanon, in 2017, and her B.S. degree from the Lebanese University, Lebanon, in 2013.

Dr. AbdulRahman completed postdoctoral research at Zayed University in Dubai, UAE, and at the LAU Artificial Intelligence & Cyber Systems Research Center. She also served as a researcher at Ericsson, Canada. Prior to joining PMU, she held teaching positions at some universities in Bahrain.

Her research interests include Artificial Intelligence, Federated Learning, Security, and Vehicular Networks. She has published extensively in high-impact journals and prestigious conferences, with over 1,500 citations. She has also contributed a book chapter and holds one patent.

She was awarded first place in the 2019 Data Science Hackathon at Ericsson, Canada. In addition, she received the Research Dissemination Award (Substance Award) in 2020 and the APÉTS Award in 2022, from ÉTS, Canada.

  • Ph.D. in Engineering - École de Technologie Supérieure - Montreal, Canada, 2022.
  • MS in Computer Science - Lebanese American University - Beirut, Lebanon, 2017.
  • BS in Management Information Systems - Lebanese University, Lebanon, 2013.

 

Journal Articles:

  1. S. AbdulRahman, H. Tout, H. Ould-Slimane, A. Mourad, C. Talhi, & M. Guizani. “A Survey on Federated Learning: The Journey from Centralized to Distributed On-Site Learning and Beyond.” IEEE Internet of Things Journal, 2020.
  2. S. AbdulRahman, H. Tout, C. Talhi, & Azzam Mourad. “Internet of Things Intrusion Detection: Centralized, On-Device, or Federated Learning?.” IEEE Network, 2020.
  3. S. AbdulRahman, H. Tout, A. Mourad, & C. Talhi. “FedMCCS: Multi Criteria Client Selection Model for Optimal IoT Federated Learning.” IEEE Internet of Things Journal, 2020.
  4. S. AbdulRahman, A. Mourad, M. El Barachi, & W. Al Orabi. “A novel on-demand vehicular sensing framework for traffic condition monitoring”. Vehicular Communications, 12, 165-178, 2018.
  5. S. AbdulRahman, A. Mourad, & M. El Barachi, “An Infrastructure-Assisted Crowdsensing Approach for On-Demand Traffic Condition Estimation,” in IEEE Access, vol. 7, pp. 163323-163340, 2019.
  6. S. AbdulRahman, S. Otoum, O. Bouachir, and A. Mourad. “Management of Digital Twin-drive IoT using Federated Learning.” IEEE Journal on Selected Areas in Communications, 2023.
  7. S. AbdulRahman, H. Ould-Slimane, R. Chowdhury, A. Mourad, C. Talhi, & M. Guizani. “Adaptive Upgrade of Client Resources for improving the quality of Federated Learning model.” IEEE Internet of Things Journal, 2022.
  8. S. AbdulRahman, O. Bouachir, S. Otoum, and A. Mourad. “CRAS-FL: Clustered resource-aware scheme for federated learning in vehicular networks.” Vehicular Communications, 2024.
  9. S. AbdulRahman, S. Otoum, and O. Bouachir. “Federated learning on the go: Building stable clusters and optimizing resources on the road.” Vehicular Communications, 2025.
  10. M Al Qerom, M Otair, F Meziane, S. AbdulRahman, and M Alzubi. “LICA-CS: Efficient Lossless Image Compression Algorithm via Column Subtraction Model.” Journal of Robotics and Control, 2024.


Conference Articles:

  1. S. AbdulRahman, O. Bouachir, S. Otoum, & A. Mourad. “Overcoming Resource Bottlenecks in Vehicular Federated Learning: A Cluster-Based & QoS-Aware Approach” In GLOBECOM 2023-IEEE Global Communications Conference (pp. 419-424), 2023.
  2. W. Al Orabi, S. AbdulRahman, A. Mourad, & M., El Barachi. “Towards on Demand Road Condition Monitoring Using Mobile Phone Sensing as a Service.” In ANT/SEIT (pp. 345-352), 2016.
  3. S. AbdulRahman and F Albalas. “From On-Board Data to Network Knowledge: A Group-Based Federated Learning Approach for Vehicular Networks.” In Finance and Law in the Metaverse World, 2024.
  4. S. AbdulRahman, O. Bouachir, S. Otoum, & A. Mourad. “Towards Boosting Federated Learning Convergence: A computation Offloading & Clustering Approach.” In ICC-IEEE International Conference on Communications (pp. 106-111), 2023.


Patents:

  1. Tout H., AbdulRahman S., Nahum, M., Soueidan, S., Talhi, C., & Mourad, A. (2023). U.S. Patent Application No. 18/041,832.


Book Chapters:

  1. M. El Barachi, S. AbdulRahman, A. Mourad, & W. Al Orabi. "Nanosensors for traffic condition monitoring." In Nanosensors for Smart Cities, pp. 187-208. Elsevier, 2020.

  • Computer Science II (GEIT1412)
  • Database I (GEIT3341)
  • Introduction to Information Technology (ITAP1311)

 

  • Artificial Intelligence
  • Federated Learning
  • Privacy & Security
  • Vehicular Networks.