Machine learning systems in the IoT: Trustworthiness trade-offs for edge intelligence

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)
20 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 2nd International Conference on Cognitive Machine Intelligence, CogMI 2020
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages177-184
Number of pages8
ISBN (Electronic)9781728141442
DOIs
Publication statusPublished - 2020
Event2nd IEEE International Conference on Cognitive Machine Intelligence, CogMI 2020 - Virtual, Atlanta, United States
Duration: 1 Dec 20203 Dec 2020

Publication series

NameProceedings - 2020 IEEE 2nd International Conference on Cognitive Machine Intelligence, CogMI 2020

Conference

Conference2nd IEEE International Conference on Cognitive Machine Intelligence, CogMI 2020
Country/TerritoryUnited States
CityVirtual, Atlanta
Period1/12/203/12/20

Keywords

  • Edge intelligence
  • Internet of Things
  • Machine learning systems
  • Smart services
  • Trade-offs
  • Trustworthiness

Fingerprint

Dive into the research topics of 'Machine learning systems in the IoT: Trustworthiness trade-offs for edge intelligence'. Together they form a unique fingerprint.

Cite this