Machine Learning Systems in the IoT: Trustworthiness Trade-offs for Edge Intelligence

• URL: http://arxiv.org/abs/2012.00419v1
• Date: Tue, 1 Dec 2020 11:42:34 GMT
• Title: Machine Learning Systems in the IoT: Trustworthiness Trade-offs for Edge Intelligence
• Authors: Wiebke Toussaint and Aaron Yi Ding
• Abstract summary: Machine learning systems (MLSys) are emerging in the Internet of Things (IoT) to provision edge intelligence. 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.
• Score: 1.2437226707039446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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