Federated Learning for Online Resource Allocation in Mobile Edge Computing: A Deep Reinforcement Learning Approach

Mar 26, 2023·
Jingjing Zheng
,
Kai Li
Naram Mhaisen
Naram Mhaisen
,
Wei Ni
,
Eduardo Tovar
,
Mohsen Guizani
Abstract
A deep reinforcement learning approach combined with federated learning for online resource allocation in mobile edge computing systems.
Type
Publication
In 2023 IEEE Wireless Communications and Networking Conference (WCNC), 1-6
publications
Federated Learning Reinforcement Learning Mobile Edge Computing
Naram Mhaisen
Authors
Naram Mhaisen
Postdoctoral Researcher

Naram is a Postdoctoral Researcher with the Networked Systems Group at the Faculty of Electrical Engineering, Mathematics, and Computer Science, TU Delft, The Netherlands. His research focuses on online learning techniques with applications in resource management across communication systems.

He received the B.Sc. degree in Computer Engineering from Qatar University in 2017 with the Order of Excellence award, and the M.Sc. degree with Distinction in Computing from Qatar University in 2020, supervised by Prof. Mohsen Guizani. He completed his Ph.D. at TU Delft in 2025, supervised by Dr. George Iosifidis, with a thesis on Optimistic Learning with Applications to Caching Networks.

His work has been recognized by the Microsoft Imagine Cup award (2017) and a NEC Europe Research Fellowship (2022).