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

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...

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Vydáno v:IEEE Communications surveys and tutorials Ročník 25; číslo 1; s. 591 - 624
Hlavní autoři: Duan, Sijing, Wang, Dan, Ren, Ju, Lyu, Feng, Zhang, Ye, Wu, Huaqing, Shen, Xuemin
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 2023
Témata:
Artificial intelligence
Cloud computing
Distributed artificial intelligence
Edge computing
end-edge-cloud computing
federated learning
network computing
Privacy
Security
Servers
Training
ISSN:2373-745X
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Shrnutí: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.
ISSN:2373-745X
DOI:10.1109/COMST.2022.3218527