Machine Learning Systems in the IoT: Trustworthiness Trade-offs for Edge Intelligence
This paper addresses the challenges of designing trustworthy edge intelligence for IoT systems by advocating a holistic design approach that considers multi-stakeholder concerns and trade-offs.
This paper analyzes the trade-offs in integrating machine learning systems (MLSys) into the Internet of Things (IoT) for edge intelligence, considering the challenges posed by heterogeneous, resource-constrained devices and decentralized operations. It covers developments up to 2020 on scaling and distributing ML across cloud, edge, and IoT devices.
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.