The union of Edge Computing (EC) and Artificial Intelligence (AI) has brought forward the Edge AI concept to provide intelligent solutions close to end-user environment, for privacy preservation, low latency to real-time performance, as well as 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 that a great attention of Edge ML is gained in both 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: we start by identifying the Edge ML requirements driven by the joint constraints. We then 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 open issues to shed light on future directions for Edge ML.

翻译：边缘计算（EC）与人工智能（AI）的融合催生了边缘AI概念，旨在为终端用户环境提供智能解决方案，以实现隐私保护、实时响应低延迟及资源优化。作为近年来AI领域最具突破性的分支，机器学习（ML）在边缘环境中已展现出令人鼓舞的成果与应用。然而，由于需同时满足边缘计算与AI领域的联合约束，边缘赋能的机器学习解决方案实现更为复杂，相应的解决方案需在数据处理、模型压缩、分布式推理及适应边缘ML需求的高级学习范式等技术中具备高效性。尽管学术界和工业界对边缘ML给予了高度关注，但我们注意到现有边缘ML技术尚未有完整的综述来建立对这一概念的共识。为此，本文旨在提供边缘ML技术的全面分类与系统性综述：首先识别由联合约束驱动的边缘ML需求，继而调研超过二十种范式与技术及其代表性工作，涵盖边缘推理与边缘学习两大核心领域，重点分析各技术如何通过满足特定需求子集适用于边缘ML。最后总结边缘ML的开放性问题，为未来研究方向提供启示。