Abstract

In this book chapter, we design and develop a mobile edge learning (MEL) framework that enables multiple end user devices or “learners” to cooperatively train a machine learning (ML) model in a wireless edge environment. We will focus on designing and developing the heterogeneity aware synchronous (HA-Sync) approach with time constraints and extend the framework to consider dual-time and energy constraints. The proposed MEL framework will include the commonly known federated learning (FL) as well as parallelized learning (PL). After discussing the system model and a brief convergence proof for both FL and PL, we will formulate the problem as a quadratically constrained integer linear program (QCILP), relax it to a QCLP, and propose analytical solutions based on Lagrangian analysis, Karush-Kuhn-Tucker (KKT) conditions, and partial fraction expansion. For the problem with dual-time and energy …