AFLPC: An Asynchronous Federated Learning Privacy-Preserving Computing Model Applied to 5G-V2X

Jie Huang, Cheng Xu, Zhaohua Ji, Shan Xiao, Teng Liu, Nan Ma, Qinghui Zhou

DOI: 10.1155/2022/9334943
Published: 2022-03-08
Container: Security and Communication Networks
Publisher: Wiley
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1155/2022/9334943,
  title = {AFLPC: An Asynchronous Federated Learning Privacy-Preserving Computing Model Applied to 5G-V2X},
  author = {Jie Huang and Cheng Xu and Zhaohua Ji and Shan Xiao and Teng Liu and Nan Ma and Qinghui Zhou},
  year = {2022},
  journal = {Security and Communication Networks},
  doi = {10.1155/2022/9334943},
  url = {https://doi.org/10.1155/2022/9334943}
}

RIS

TY  - JOUR
TI  - AFLPC: An Asynchronous Federated Learning Privacy-Preserving Computing Model Applied to 5G-V2X
AU  - Jie Huang
AU  - Cheng Xu
AU  - Zhaohua Ji
AU  - Shan Xiao
AU  - Teng Liu
AU  - Nan Ma
AU  - Qinghui Zhou
PY  - 2022
JO  - Security and Communication Networks
DO  - 10.1155/2022/9334943
UR  - https://doi.org/10.1155/2022/9334943
ER  -

APA

Huang, J., Xu, C., Ji, Z., Xiao, S., Liu, T., Ma, N., & Zhou, Q. (2022). AFLPC: An Asynchronous Federated Learning Privacy-Preserving Computing Model Applied to 5G-V2X. Security and Communication Networks. https://doi.org/10.1155/2022/9334943

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crossref · retrieved 2026-09-25T01:28:34.718Z