边缘机器学习的全面综述与分类法：需求、范式与技术
A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques
AI总结：
本文针对边缘计算与AI结合缺乏完整综述的问题，提出Edge ML技术的全面分类法与系统回顾，涵盖边缘推理与边缘学习等二十多种范式，并总结框架与未来方向。
摘要：
边缘计算（EC）与人工智能（AI）的结合推动了 Edge AI 概念的发展，旨在提供贴近终端用户环境的智能解决方案，以实现隐私保护、低延迟的实时性能以及资源优化。机器学习（ML）作为过去几年AI中最先进的分支，在边缘环境中展现出了令人鼓舞的成果与应用。然而，由于边缘计算和AI领域的双重联合约束，实现边缘驱动的ML解决方案更为复杂，且相应的解决方案需要在数据处理、模型压缩、分布式推理以及面向 Edge ML 需求的先进学习范式等技术上保持高效和适应性。尽管 Edge ML 在学术界和工业界都获得了极大关注，我们仍注意到缺乏对现有 Edge ML 技术的完整综述来提供对该概念的共同理解。为解决此问题，本文旨在提供 Edge ML 技术的全面分类法和系统性回顾，重点关注现有范式与技术的软计算方面。我们首先识别由联合约束驱动的 Edge ML 需求。然后，我们广泛调研了二十多种范式和技术及其代表性工作，涵盖两个主要部分：边缘推理和边缘学习。特别地，我们分析了每种技术如何通过满足所识别需求的子集来融入 Edge ML。我们还总结了 Edge ML 框架和开放性问题，为 Edge ML 的未来方向提供启示。
英文摘要：
The union of Edge Computing (EC) and Artificial Intelligence (AI) has brought forward the Edge AI concept to provide intelligent solutions close to the end-user environment, for privacy preservation, low latency to real-time performance, and resource optimization. Machine Learning (ML), as the most advanced branch of AI in the past few years, has shown encouraging results and applications in the edge environment. Nevertheless, edge-powered ML solutions are more complex to realize due to the joint constraints from both edge computing and AI domains, and the corresponding solutions are expected to be efficient and adapted in technologies such as data processing, model compression, distributed inference, and advanced learning paradigms for Edge ML requirements. Despite the fact that a great deal of the attention garnered by Edge ML is gained in both the academic and industrial communities, we noticed the lack of a complete survey on existing Edge ML technologies to provide a common understanding of this concept. To tackle this, this paper aims at providing a comprehensive taxonomy and a systematic review of Edge ML techniques, focusing on the soft computing aspects of existing paradigms and techniques. We start by identifying the Edge ML requirements driven by the joint constraints. We then extensively survey more than twenty paradigms and techniques along with their representative work, covering two main parts: edge inference, and edge learning. In particular, we analyze how each technique fits into Edge ML by meeting a subset of the identified requirements. We also summarize Edge ML frameworks and open issues to shed light on future directions for Edge ML.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
