4 citations · 7 across the 2 of their papers we have counts for
5 papers
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…
Adaptive Task Allocation for Asynchronous Federated and Parallelized Mobile Edge Learning
Umair Mohammad, Sameh Sorour
This paper proposes a scheme to efficiently execute distributed learning tasks in an asynchronous manner while minimizing the gradient staleness on wireless edge nodes with heterog…
Adaptive Task Allocation for Mobile Edge Learning
Umair Mohammad, Sameh Sorour
This paper aims to establish a new optimization paradigm for implementing realistic distributed learning algorithms, with performance guarantees, on wireless edge nodes with hetero…