University of Oulu

Y. Jiang, Y. Wu, F. -C. Zheng, M. Bennis and X. You, "Federated Learning-Based Content Popularity Prediction in Fog Radio Access Networks," in IEEE Transactions on Wireless Communications, vol. 21, no. 6, pp. 3836-3849, June 2022, doi: 10.1109/TWC.2021.3124586

Federated learning-based content popularity prediction in fog radio access networks

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Author: Jiang, Yanxiang1,2; Wu, Yuting1; Zheng, Fu-Chun1,2;
Organizations: 1National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China
2School of Electronic and Information Engineering, Harbin Institute of Technology, Shenzhen 518055, China
3Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.8 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-08-26


In this paper, the content popularity prediction problem in fog radio access networks (F-RANs) is investigated. In order to obtain accurate prediction with low complexity, we propose a novel context-aware popularity prediction policy based on federated learning (FL). Firstly, user preference learning is applied by considering that users prefer to request the contents they are interested in. Then, users’ context information is utilized to cluster users efficiently by adaptive context space partitioning. After that, we formulate a popularity prediction optimization problem to learn the local model parameters by using the stochastic variance reduced gradient (SVRG) algorithm. Finally, FL based model integration is proposed to learn the global popularity prediction model based on local models using the distributed approximate Newton (DANE) algorithm with SVRG. Our proposed popularity prediction policy not only can predict content popularity accurately, but also can significantly reduce computational complexity. Moreover, we theoretically analyze the convergence bound of our proposed FL based model integration algorithm. Simulation results show that our proposed policy increases the cache hit rate by up to 21.5 % compared to existing policies.

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Series: IEEE transactions on wireless communications
ISSN: 1536-1276
ISSN-E: 1558-2248
ISSN-L: 1536-1276
Volume: 21
Issue: 6
Pages: 3836 - 3849
DOI: 10.1109/twc.2021.3124586
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported in part by the National Natural Science Foundation of China under Grant 61971129, the National Key Research and Development Program under Grant 2021YFB2900300, and the Shenzhen Science and Technology Program under Grant KQTD20190929172545139.
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