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This paper extends the paradigm of "mobile edge learning (MEL)" by designingan optimal task allocation scheme for training a machine learning model in anasynchronous manner across mutiple edge nodes or learners connected via aresource-constrained wireless edge network. The optimization is done such thatthe portion of the task allotted to each learner is completed within a givenglobal delay constraint and a local maximum energy consumption limit. The timeand energy consumed are related directly to the heterogeneous communication andcomputational capabilities of the learners; i.e. the proposed model isheterogeneity aware (HA). Because the resulting optimization is an NP-hardquadratically-constrained integer linear program (QCILP), a two-stepsuggest-and-improve (SAI) solution is proposed based on using the solution ofthe relaxed synchronous problem to obtain the solution to the asynchronousproblem. The proposed HA asynchronous (HA-Asyn) approach is compared againstthe HA synchronous (HA-Sync) scheme and the heterogeneity unaware (HU) equalbatch allocation scheme. Results from a system of 20 learners tested forvarious completion time and energy consumption constraints show that theproposed HA-Asyn method works better than the HU synchronous/asynchronous(HU-Sync/Asyn) approach and can provide gains of up-to 25\% compared to theHA-Sync scheme.

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