Energy-Efficient Device Assignment and Task Allocation in Multi-Orchestrator Mobile Edge Learning

Abstract

Mobile Edge Learning (MEL) is a decentralized learning paradigm that enables resource-constrained IoT devices to either learn a shared model without sharing the data, or to distribute the learning task with the data to other IoT devices and utilize their available resources. In the former case, IoT devices (a.k.a learners) need to be assigned an orchestrator to facilitate the learning and models' aggregation from different learners. Whereas in the latter case, IoT devices act as orchestrators and look for learners with available resources to distribute the learning task to. However, the coexistence of multiple learning problems in an environment with limited resources poses the learners-orchestrator assignment problem. To this end, we aim to develop an energy-efficient learner assignment and task allocation scheme, in which each orchestrator gets assigned a group of learners based on their communication channel qualities and computational resources. We formulate and solve a multi-objective optimization problem to minimize the total energy consumption and maximize the learning accuracy. To reduce the solution complexity, we also propose a lightweight heuristic algorithm that can achieve near-optimal performance. The conducted simulations show that our proposed approaches can execute multiple learning tasks efficiently and significantly reduce energy consumption compared to current state-of-art methods.