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Cited by 408

The DOI matches its Crossref record; 263 of the 263 references it cites were found in a registry or an index.

Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A Survey

Duan, Sijing, Wang, Dan, Ren, Ju, Lyu, Feng, Zhang, Ye, Wu, Huaqing, Shen, Xuemin·2023·IEEE Communications Surveys & Tutorials
DOI 10.1109/comst.2022.3218527
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Abstract

As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it also supports artificial intelligence evolving from a centralized manner to a distributed one. In this paper, we provide a comprehensive survey on the distributed artificial intelligence (DAI) empowered by end-edge-cloud computing (EECC), where the heterogeneous capabilities of on-device computing, edge computing, and cloud computing are orchestrated to satisfy the diverse requirements raised by resource-intensive and distributed AI computation. Particularly, we first introduce several mainstream computing paradigms and the benefits of the EECC paradigm in supporting distributed AI, as well as the fundamental technologies for distributed AI. We then derive a holistic taxonomy for the state-of-the-art optimization technologies that are empowered by EECC to boost distributed training and inference, respectively. After that, we point out security and privacy threats in DAI-EECC architecture and review the benefits and shortcomings of each enabling defense technology in accordance with the threats. Finally, we present some promising applications enabled by DAI-EECC and highlight several research challenges and open issues toward immersive performance acquisition.

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Citations

Cites

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Metadata

Authors
Duan, S., Wang, D., Ren, J., Lyu, F., Zhang, Y., Wu, H., & Shen, X.
Venue
IEEE Communications Surveys & Tutorials
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2023
Volume
25
Issue
1
Pages
591-624
ISSN
  • 1553-877X (ISSN-L)
  • 2373-745X
DOI
10.1109/comst.2022.3218527
OpenAlex
W4312588618
Cited by
408
References
263
FWCI
35.25
Type
journal article
Language
en
Open access
closed
Licence
Funders
  • National Key Research and Development Program of China (2022YFF0604502)
  • National Natural Science Foundation of China (62122095, 62002389, 62072472, U19A2067)
  • Grant from the Guoqiang Institute, Tsinghua University
  • Natural Science Foundation of Hunan Province, China (2020JJ2050, 2021JJ20079)
  • Young Elite Scientist Sponsorship Program by CAST (YESS20200238)
  • Young Talents Plan of Hunan Province of China (2021RC3004)
  • Higher Education Discipline Innovation Project (B18059)
  • Central South University Innovation-Driven Research Programme (2023CXQD029)

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CoChat. (2026). Citation check: Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A Survey. https://app.cochat.ai/papers/10.1109/comst.2022.3218527
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Duan, S., Wang, D., Ren, J., Lyu, F., Zhang, Y., Wu, H., & Shen, X. (2023). Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A Survey. IEEE Communications Surveys & Tutorials. https://doi.org/10.1109/comst.2022.3218527

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