most citedFLCC: Efficient Distributed Federated Learning on IoMT over CSMA/CA

1 citations · 1 across the 1 of their papers we have counts for

collaborators

5 papers

eess.SP20253 cited

Agentic AI meets Neural Architecture Search: Proactive Traffic Prediction for AI-RAN

Abdelaziz Salama, Mohammed M. H. Qazzaz, Zeinab Nezami +2

Next-generation wireless networks require intelligent traffic prediction to enable autonomous resource management and handle diverse, dynamic service demands. The Open Radio Access…

eess.SY2025

Semantic-Aware Edge Intelligence for UAV Handover in 6G Networks

Aubida A. Al-Hameed, Mohammed M. H. Qazzaz, Maryam Hafeez +1

6G wireless networks aim to exploit semantic awareness to optimize radio resources. By optimizing the transmission through the lens of the desired goal, the energy consumption of t…

eess.SY2025

FedORA: Resource Allocation for Federated Learning in ORAN using Radio Intelligent Controllers

Abdelaziz Salama, Mohammed M. H. Qazzaz, Syed Danial Ali Shah +2

This work proposes an integrated approach for optimising Federated Learning (FL) communication in dynamic and heterogeneous network environments. Leveraging the modular flexibility…

eess.SP20241 cited

Machine Learning-based xApp for Dynamic Resource Allocation in O-RAN Networks

Mohammed M. H. Qazzaz, Łukasz Kułacz, Adrian Kliks +3

The disaggregated, distributed and virtualised implementation of radio access networks allows for dynamic resource allocation. These attributes can be realised by virtue of the Ope…

cs.DC20231 cited

FLCC: Efficient Distributed Federated Learning on IoMT over CSMA/CA

Abdelaziz Salama, Syed Ali Zaidi, Des McLernon +1

Federated Learning (FL) has emerged as a promising approach for privacy preservation, allowing sharing of the model parameters between users and the cloud server rather than the ra…