Print Email Facebook Twitter Machine learning systems in the IoT Title Machine learning systems in the IoT: Trustworthiness trade-offs for edge intelligence Author Hutiri, Wiebke (TU Delft Information and Communication Technology) Ding, Aaron Yi (TU Delft Information and Communication Technology) Date 2020 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 tradeoffs 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. Subject Edge intelligenceInternet of ThingsMachine learning systemsSmart servicesTrade-offsTrustworthiness To reference this document use: http://resolver.tudelft.nl/uuid:6956f8c5-1c40-4091-a1bd-dae8bbb31725 DOI https://doi.org/10.1109/CogMI50398.2020.00030 Publisher Institute of Electrical and Electronics Engineers (IEEE) ISBN 9781728141442 Source Proceedings - 2020 IEEE 2nd International Conference on Cognitive Machine Intelligence, CogMI 2020 Event 2nd IEEE International Conference on Cognitive Machine Intelligence, CogMI 2020, 2020-12-01 → 2020-12-03, Virtual, Atlanta, United States Series Proceedings - 2020 IEEE 2nd International Conference on Cognitive Machine Intelligence, CogMI 2020 Part of collection Institutional Repository Document type conference paper Rights © 2020 Wiebke Hutiri, Aaron Yi Ding Files PDF IEEE_CogMI_2020_paper_20.pdf 220.28 KB Close viewer /islandora/object/uuid:6956f8c5-1c40-4091-a1bd-dae8bbb31725/datastream/OBJ/view