25 citations · 98 across the 33 of their papers we have counts for
41 papers
Spatiotemporal Analysis of Parallelized Computing at the Extreme Edge
Yasser Nabil, Mahmoud Abdelhadi, Sameh Sorour +3
Extreme Edge Computing (EEC) pushes computing even closer to end users than traditional Multi-access Edge Computing (MEC), harnessing the idle resources of Extreme Edge Devices (EE…
Energy-Efficient Multi-Orchestrator Mobile Edge Learning
Mhd Saria Allahham, Sameh Sorour, Amr Mohamed +2
Mobile Edge Learning (MEL) is a collaborative learning paradigm that features distributed training of Machine Learning (ML) models over edge devices (e.g., IoT devices). In MEL, po…
Motivating Learners in Multi-Orchestrator Mobile Edge Learning: A Stackelberg Game Approach
Mhd Saria Allahham, Sameh Sorour, Amr Mohamed +2
Mobile Edge Learning (MEL) is a learning paradigm that enables distributed training of Machine Learning models over heterogeneous edge devices (e.g., IoT devices). Multi-orchestrat…
Task Allocation for Asynchronous Mobile Edge Learning with Delay and Energy Constraints
Umair Mohammad, Sameh Sorour, Mohamed Hefeida
This paper extends the paradigm of "mobile edge learning (MEL)" by designing an optimal task allocation scheme for training a machine learning model in an asynchronous manner acros…
Returning the Favor: What Wireless Networking Can Offer to AI and Edge Learning
Sameh Sorour, Umair Mohammad, Amr Abutuleb +1
Machine learning (ML) and artificial intelligence (AI) have recently made a significant impact on improving the operations of wireless networks and establishing intelligence at the…
Jointly Optimizing Dataset Size and Local Updates in Heterogeneous Mobile Edge Learning
Umair Mohammad, Sameh Sorour, Mohamed Hefeida
This paper proposes to maximize the accuracy of a distributed machine learning (ML) model trained on learners connected via the resource-constrained wireless edge. We jointly optim…