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 EL, synthesize existing knowledge, identify challenges, and highlight future trends. Utilizing Scopus' advanced search, relevant literature on EL was identified, revealing a concentration of research efforts in distributed learning methods, particularly Federated Learning (FL). This survey further provides a guideline for comparing techniques used to optimize ML for edge learning, along with an exploration of different frameworks, libraries, and simulation tools available for EL. 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 edge learning.

翻译：边缘计算（EC）近年来发展迅猛，通过将人工智能（AI）能力集成至边缘端，有效提升了系统效率。尽管当前研究主要聚焦于边缘端机器学习（ML）模型的部署与推理，其训练环节仍较少被探索。本综述深入探讨边缘学习（EL），重点关注边缘端机器学习模型训练的优化问题。研究目标包括：全面梳理EL领域的多元方法与技术体系、整合现有知识成果、识别关键挑战，并展望未来发展趋势。通过Scopus高级检索筛选相关文献，我们发现当前研究高度集中于分布式学习方法，特别是联邦学习（FL）。本综述进一步提供了边缘学习优化技术比较的指导框架，系统梳理了可用的EL框架、函数库与仿真工具。由此，本文致力于构建边缘计算与机器学习交叉领域当前图景及未来方向的整体认知，为边缘学习场景下各优化方法与技术的系统性对比奠定基础。