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）近年来获得了显著发展，通过在边缘集成人工智能（AI）能力，有望提升效率。尽管研究重点主要集中在边缘机器学习（ML）模型的部署与推理，但其训练方面仍较少被探索。本综述深入探讨了边缘学习（EL），特别是边缘ML模型训练的优化问题。目标是全面探索EL中的各种方法和技术，综合现有知识，识别挑战并突出未来趋势。利用Scopus的高级检索功能，我们识别了相关EL文献，发现研究主要集中在分布式学习方法，特别是联邦学习（FL）。本综述进一步提供了比较边缘学习ML优化技术的指南，并探讨了可用的不同EL框架、库和仿真工具。通过上述工作，本文有助于全面理解边缘计算与机器学习交叉领域的当前格局及未来方向，为边缘学习优化方法和技术之间的知情比较奠定基础。