Training Machine Learning models at the Edge: A Survey

arXiv:2403.02619v1 Announce Type: new
Abstract: Edge Computing (EC) has gained significant traction in recent years, promising enhanced efficiency by integrating Artificial Intelligence (AI) 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 delves into Edge Learning (EL), specifically the optimization of ML model training at the edge. The objective is to comprehensively explore diverse approaches and methodologies in …

abstract artificial artificial intelligence arxiv capabilities computing cs.dc cs.lg deployment edge edge computing efficiency focus inference intelligence machine machine learning machine learning models survey the edge training type