Content Popularity Prediction Based on Quantized Federated Bayesian Learning in Fog Radio Access Networks-Reference-Cited by-同舟云学术

Content Popularity Prediction Based on Quantized Federated Bayesian Learning in Fog Radio Access Networks

Published:2023-02 Issue:2 Volume:71 Page:893-907
ISSN:0090-6778
Container-title:IEEE Transactions on Communications
language:
Short-container-title:IEEE Trans. Commun.

Author:

Tao Yunwei¹^ORCID,Jiang Yanxiang¹^ORCID,Zheng Fu-Chun¹^ORCID,Wang Zhiheng¹,Zhu Pengcheng¹^ORCID,Tao Meixia²^ORCID,Niyato Dusit³^ORCID,You Xiaohu¹^ORCID

Affiliation:

1. National Mobile Communications Research Laboratory, Southeast University, Nanjing, China

2. Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China

3. School of Computer Engineering, Nanyang Technological University (NTU), Jurong West, Singapore

Funder

National Natural Science Foundation of China

National Key Research and Development Program

Shenzhen Science and Technology Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/26/10045341/10004711.pdf?arnumber=10004711

Reference51 articles.

1. Deep Learning-Based Edge Caching in Fog Radio Access Networks

2. DFW-PP: Dynamic feature weighting based popularity prediction for social media content;reddy g;arXiv 2110 08510,2021

3. Federated Learning Based Content Popularity Prediction in Fog Radio Access Networks

5. Contextual Learning for Content Caching With Unknown Time-Varying Popularity Profiles via Incremental Clustering

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