Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness

Yuwei Sun, Hideya Ochiai, Hiroshi Esaki

Open source

DOI: 10.1109/tai.2021.3133819
Published: 2022-12
Container: IEEE Transactions on Artificial Intelligence
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1109/tai.2021.3133819,
  title = {Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness},
  author = {Yuwei Sun and Hideya Ochiai and Hiroshi Esaki},
  year = {2022},
  journal = {IEEE Transactions on Artificial Intelligence},
  doi = {10.1109/tai.2021.3133819},
  url = {https://doi.org/10.1109/tai.2021.3133819}
}

RIS

TY  - JOUR
TI  - Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness
AU  - Yuwei Sun
AU  - Hideya Ochiai
AU  - Hiroshi Esaki
PY  - 2022
JO  - IEEE Transactions on Artificial Intelligence
DO  - 10.1109/tai.2021.3133819
UR  - https://doi.org/10.1109/tai.2021.3133819
ER  -

APA

Sun, Y., Ochiai, H., & Esaki, H. (2022). Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness. IEEE Transactions on Artificial Intelligence. https://doi.org/10.1109/tai.2021.3133819

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crossref · retrieved 2026-09-25T20:32:54.140Z