Dynamic Task Allocation for Mobile Edge Learning

Umair Mohammad

doi:10.1109/TMC.2021.3137017

Dynamic Task Allocation for Mobile Edge Learning

2021-01-01

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DOI：10.1109/TMC.2021.3137017

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Abstract

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This paper introduces the new paradigm of Mobile Edge Learning MEL that enables the implementation of realistic distributed machine learning (DML) tasks on wireless edge nodes while taking into consideration the heterogeneous computing and networking environments. Therefore, a heterogeneity aware (HA) scheme is designed to solve the problem of dynamic task allocation for MEL in a way that maximizes the DML accuracy over wireless heterogeneous nodes or 'learners' while respecting the time constraints. The problem is first formulated as a quadratically-constrained integer linear program (QCILP). Being NP-hard, it is relaxed into a non-convex problem over real variables which can be solved using commercially available numerical solvers. The relaxation also allows us to propose a solution based on deriving the analytical upper bounds of the optimal solution using Lagrangian analysis and Karush-Kuhn-Tucker (KKT) conditions. The merits of the proposed analytical solution are demonstrated by comparing its performance to the numerical approaches and comparing the validation accuracy of the proposed HA scheme to the baseline heterogeneity unaware (HU) equal task allocation approach. Simulation results show that the HA schemes decrease convergence time up-to 56% and increase the final validation accuracy up-to 8%.

Keywords:

Task analysis

Resource management

Computational modeling

Wireless communication

Servers

Machine learning

Analytical models

Mobile edge learning

dynamic task allocation

distributed machine learning

mobile edge computing

federated learning

parallelized learning

Dynamic Task Allocation for Mobile Edge Learning

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