论文标题
物联网中的机器学习系统：边缘情报的可信赖性权衡
Machine Learning Systems in the IoT: Trustworthiness Trade-offs for Edge Intelligence
论文作者
论文摘要
机器学习系统（MLSYS）正在物联网（IoT）中出现，以供应边缘智能，这正在朝着无处不在的智能视野铺平道路。但是，尽管机器学习系统和物联网的成熟度成熟，但在实践背景下整合MLSY和IoT时，我们仍面临严重的挑战。例如，已经开发了许多用于大规模生产的机器学习系统（例如，云环境），但是由于异质和资源受限的设备以及分散的操作环境，IoT引入了其他需求。为了阐明MLSYS和IoT的这种融合，本文通过涵盖了跨云，边缘和物联网设备的缩放和分配ML的最新开发（持续时间为2020年），从而分析了权衡。我们将机器学习系统定位为物联网的组成部分，而Edge Intelligence则作为社会技术系统。关于设计值得信赖的边缘情报的挑战，我们主张一种整体设计方法，该方法将多方利益相关者的疑虑，设计要求和权衡考虑，并强调了Edge Intelligence的未来研究机会。
Machine learning systems (MLSys) are emerging in the Internet of Things (IoT) to provision edge intelligence, which is paving our way towards the vision of ubiquitous intelligence. However, despite the maturity of machine learning systems and the IoT, we are facing severe challenges when integrating MLSys and IoT in practical context. For instance, many machine learning systems have been developed for large-scale production (e.g., cloud environments), but IoT introduces additional demands due to heterogeneous and resource-constrained devices and decentralized operation environment. To shed light on this convergence of MLSys and IoT, this paper analyzes the trade-offs by covering the latest developments (up to 2020) on scaling and distributing ML across cloud, edge, and IoT devices. We position machine learning systems as a component of the IoT, and edge intelligence as a socio-technical system. On the challenges of designing trustworthy edge intelligence, we advocate a holistic design approach that takes multi-stakeholder concerns, design requirements and trade-offs into consideration, and highlight the future research opportunities in edge intelligence.
