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

TL;DR

This paper analyzes the integration of machine learning systems with IoT devices, focusing on trustworthiness trade-offs and the challenges of deploying edge intelligence in resource-constrained, decentralized environments.

Contribution

It provides a comprehensive analysis of the latest developments and trade-offs in scaling and distributing ML across cloud, edge, and IoT devices, emphasizing trustworthiness.

Findings

Identifies key challenges in deploying MLSys in IoT environments.

Highlights the importance of holistic design considering multi-stakeholder concerns.

Suggests future research directions for trustworthy edge intelligence.

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.