边缘机器学习的综合综述和分类:需求、范式和技术

IF 3.1 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Wenbin Li, Hakim Hacid, Ebtesam Almazrouei, Merouane Debbah
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引用次数: 0

摘要

边缘计算(EC)和人工智能(AI)的结合,提出了Edge AI概念,提供接近最终用户环境的智能解决方案,实现隐私保护、低延迟到实时性能和资源优化。机器学习(ML)作为近年来人工智能最先进的分支,在边缘环境中取得了令人鼓舞的成果和应用。然而,由于边缘计算和人工智能领域的联合约束,边缘驱动的机器学习解决方案更加复杂,并且相应的解决方案预计将在数据处理、模型压缩、分布式推理和边缘机器学习需求的高级学习范例等技术中高效和适应。尽管Edge ML在学术界和工业界都获得了大量关注,但我们注意到缺乏对现有Edge ML技术的完整调查,以提供对这一概念的共同理解。为了解决这个问题,本文旨在提供一个全面的分类和边缘机器学习技术的系统回顾,重点关注现有范例和技术的软计算方面。我们首先确定由联合约束驱动的边缘机器学习需求。然后,我们广泛地调查了20多个范例和技术及其代表工作,涵盖了两个主要部分:边缘推理和边缘学习。特别是,我们通过满足已确定需求的子集来分析每种技术如何适合Edge ML。我们还总结了边缘机器学习框架和开放问题,以阐明边缘机器学习的未来方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques
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.
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来源期刊
CiteScore
7.20
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