Professor Hajar El Hammouti from UM6P - Communication systems
Date: - Location: Eurecom
Abstract : Federated learning (FL) is a distributed learning framework that operates effectively over wireless networks. It enables devices to collaboratively train a model over wireless links by sharing model parameters rather than personal data. However, a key challenge in FL arises from the limited computational and communication resources of devices. As a result, energy efficiency has become a critical concern for real-world deployments. In this talk, we provide an overview of key strategies to reduce computational and communication overhead in FL systems. We will also present our recent contributions in this area, including the design of novel algorithms for client selection, model compression, and quantization. These techniques aim to minimize energy consumption while preserving model accuracy and ensuring FL convergence. Keywords: Energy Efficiency; Federated Learning; Internet of Things, Optimization; Wireless Communications. Bio: Hajar El Hammouti is an Assistant Professor at the College of Computing of Mohammed VI Polytechnic University (UM6P) in Morocco. She received her Ph.D. in Electrical Engineering from the National Institute of Posts and Telecommunications (INPT), Rabat, and held postdoctoral positions at the International University of Rabat (UIR) and King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Her research focuses on AI-driven resource management for UAV-enabled, RIS-assisted, and federated wireless networks. She is an African Research Initiative for Scientific Excellence (ARISE) Fellow and a Fulbright alumni. Additionally, she serves as a Technical Program Committee Member for prestigious conferences in wireless communications such as IEEE ICC, IEEE GLOBECOM, and IEEE WCNC, and top-tier journals including IEEE Transactions on Mobile Computing, IEEE Journal of IoT, IEEE Communications Letters, etc.
