Task Allocation for Mobile Federated and Offloaded Learning with Energy and Delay Constraints
Umair Mohammad, Sameh Sorour, Mohamed Hefeida · 2020
This paper proposes a framework to support federated learning or mobile edge learning (MEL) when there are both, delay constraints on the learning process and constraints on the energy consumed by each device. The aim is to maximize learning accuracy while guaranteeing that the total time taken and energy consumed by each learner in the system are bounded by a preset duration and maximum energy, respectively, while taking into account heterogeneous communication and communication capabilities of the channels and nodes. The problem of interest is shown to be NP-hard and a suggest-and-improve (SAI) approach is proposed based on the solution of the Lagrangian Dual problem (suggest) followed by a local optimizer based on coordinate descent (the improve step). The merits of this proposed solution, which is heterogeneity aware (HA), are exhibited by comparing its performances to both numerical approaches and the heterogeneity unaware (HU) approach.