Fueled by the availability of more data and computing power, recent
breakthroughs in cloud-based machine learning (ML) have transformed every
aspect of our lives from face recognition and medical diagnosis to natural
language processing. However, classical ML exerts severe demands in terms of
energy, memory and computing resources, limiting their adoption for resource
constrained edge devices. The new breed of intelligent devices and high-stake
applications (drones, augmented/virtual reality, autonomous systems, etc.),
requires a novel paradigm change calling for distributed, low-latency and
reliable ML at the wireless network edge (referred to as edge ML). In edge ML,
training data is unevenly distributed over a large number of edge nodes, which
have access to a tiny fraction of the data. Moreover training and inference is
carried out collectively over wireless links, where edge devices communicate
and exchange their learned models (not their private data). In a first of its
kind, this article explores key building blocks of edge ML, different neural
network architectural splits and their inherent tradeoffs, as well as
theoretical and technical enablers stemming from a wide range of mathematical
disciplines. Finally, several case studies pertaining to various high-stake
applications are presented demonstrating the effectiveness of edge ML in
unlocking the full potential of 5G and beyond.

本文探讨了边缘机器学习的关键构建块，神经网络架构的不同分裂及其内在的权衡，以及来自广泛数学学科的理论和技术促进因素，最终呈现了几个关于各种高风险应用的案例研究，展示了边缘机器学习在发挥 5G 及其以后的全部潜力方面的有效性。