Task Allocation for Asynchronous Mobile Edge Learning with Delay and Energy Constraints

no code implementations • 30 Nov 2020 • Umair Mohammad, Sameh Sorour, Mohamed Hefeida

The proposed HA asynchronous (HA-Asyn) approach is compared against the HA synchronous (HA-Sync) scheme and the heterogeneity unaware (HU) equal batch allocation scheme.

Jointly Optimizing Dataset Size and Local Updates in Heterogeneous Mobile Edge Learning

no code implementations • 12 Jun 2020 • 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.

Adaptive Task Allocation for Asynchronous Federated and Parallelized Mobile Edge Learning

no code implementations • 5 May 2019 • 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 heterogeneous computing and communication capacities.

Adaptive Task Allocation for Mobile Edge Learning

no code implementations • 9 Nov 2018 • 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 heterogeneous computing and communication capacities.