Open Access

Institute of Electrical and Electronics Engineers (IEEE)

IEEE Communications Surveys and Tutorials

, volume 25 , issue 1 , pages 591-624

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

Sijing Duan ¹

Dan Wang ¹

Ju Ren ²

Feng Lyu ¹

Ye Zhang ³

Huaqing Wu ⁴

Xuemin Sherman Shen ⁵

Hide authors affiliations Show authors affiliations: 5 affiliations

School of Computer Science and Engineering, Central South University, ChangSha, China |

Department of Computer Science and Technology, BNRist, Tsinghua University, Beijing, China |

Computer School, Beijing Information Science and Technology University, Beijing, China |

⁴

Department of Electrical and Software Engineering, University of Calgary, Calgary, Canada |

⁵

Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada |

Publication type: Journal Article

Publication date: 2023-01-01

Institute of Electrical and Electronics Engineers (IEEE)

IEEE Communications Surveys and Tutorials

scimago Q1

wos Q1

SJR: 14.184

CiteScore: 86.2

Impact factor: 46.7

ISSN: 1553877X, 2373745X

DOI: 10.1109/comst.2022.3218527

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Electrical and Electronic Engineering

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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GOST Copy

Duan S. et al. Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A Survey // IEEE Communications Surveys and Tutorials. 2023. Vol. 25. No. 1. pp. 591-624.

GOST all authors (up to 50) Copy

Duan S., Wang D., Ren J., Lyu F., Zhang Y., Wu H., Shen X. S. Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A Survey // IEEE Communications Surveys and Tutorials. 2023. Vol. 25. No. 1. pp. 591-624.

RIS |

Cite this

RIS Copy

TY - JOUR

DO - 10.1109/comst.2022.3218527

UR - https://doi.org/10.1109/comst.2022.3218527

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

T2 - IEEE Communications Surveys and Tutorials

AU - Duan, Sijing

AU - Wang, Dan

AU - Ren, Ju

AU - Lyu, Feng

AU - Zhang, Ye

AU - Wu, Huaqing

AU - Shen, Xuemin Sherman

PY - 2023

DA - 2023/01/01

PB - Institute of Electrical and Electronics Engineers (IEEE)

SP - 591-624

IS - 1

VL - 25

SN - 1553-877X

SN - 2373-745X

ER -

BibTex |

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BibTex (up to 50 authors) Copy

@article{2023_Duan,

author = {Sijing Duan and Dan Wang and Ju Ren and Feng Lyu and Ye Zhang and Huaqing Wu and Xuemin Sherman Shen},

title = {Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A Survey},

journal = {IEEE Communications Surveys and Tutorials},

year = {2023},

volume = {25},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

month = {jan},

url = {https://doi.org/10.1109/comst.2022.3218527},

number = {1},

pages = {591--624},

doi = {10.1109/comst.2022.3218527}

}

MLA

Cite this

MLA Copy

Duan, Sijing, et al. “Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A Survey.” IEEE Communications Surveys and Tutorials, vol. 25, no. 1, Jan. 2023, pp. 591-624. https://doi.org/10.1109/comst.2022.3218527.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Journal

IEEE Communications Surveys and Tutorials

scimago Q1

wos Q1

SJR

14.184

CiteScore

86.2

Impact factor

46.7

ISSN

1553877X (Print, Electronic)

2373745X

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