Training Machine Learning Models at the Edge: A Survey

Abstract

Edge computing has gained significant traction in recent years, promising enhanced efficiency by integrating artificial intelligence capabilities at the edge. While the focus has primarily been on the deployment and inference of Machine Learning (ML) models at the edge, the training aspect remains less explored. This survey explores the concept of edge learning, specifically the optimization of ML model training at the edge. The objective is to comprehensively explore diverse approaches and methodologies in edge learning, synthesize existing knowledge, identify challenges, and highlight future trends. Utilizing Scopus and Web of Science advanced search, relevant literature on edge learning was identified, revealing a concentration of research efforts in distributed learning methods, particularly federated learning. This survey further provides a guideline for comparing techniques used to optimize ML for edge learning, along with an exploration of the different frameworks, libraries, and simulation tools available. In doing so, the paper contributes to a holistic understanding of the current landscape and future directions in the intersection of edge computing and machine learning, paving the way for informed comparisons between optimization methods and techniques designed for training at the edge.