University of Oulu

Y. Wu, Y. Jiang, M. Bennis, F. Zheng, X. Gao and X. You, "Content Popularity Prediction in Fog Radio Access Networks: A Federated Learning Based Approach," ICC 2020 - 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020, pp. 1-6, doi: 10.1109/ICC40277.2020.9148697

Content popularity prediction in fog radio access networks : a federated learning based approach

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Author: Wu, Yuting1,2,3; Jiang, Yanxiang1,2,3; Bennis, Mehdi4;
Organizations: 1National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China
2State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China
3Key Laboratory of Wireless Sensor Network & Communication, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, 865 Changning Road, Shanghai 200050, China
4Centre for Wireless Communications, University of Oulu, Oulu 90014, Finland
5School of Electronic and Information Engineering, Harbin Institute of Technology, Shenzhen 518055, China
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.3 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2020100678118
Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-10-06
Description:

Abstract

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. 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 using the stochastic variance reduced gradient (SVRG) algorithm. Finally, federated learning based model integration is proposed to construct the global popularity prediction model based on local models by combining the distributed approximate Newton (DANE) algorithm with SVRG. Our proposed popularity prediction policy not only predicts content popularity accurately, but also significantly reduces computational complexity. Simulation results show that our proposed policy increases the cache hit rate by up to 21.5 % compared to the traditional policies.

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Series: IEEE International Conference on Communications
ISSN: 1550-3607
ISSN-E: 1938-1883
ISSN-L: 1550-3607
ISBN: 978-1-7281-5089-5
ISBN Print: 978-1-7281-5090-1
Pages: 1 - 6
Article number: 9148697
DOI: 10.1109/ICC40277.2020.9148697
OADOI: https://oadoi.org/10.1109/ICC40277.2020.9148697
Host publication: 2020 IEEE International Conference on Communications, ICC 2020
Conference: IEEE International Conference on Communications
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This work was supported in part by the Natural Science Foundation of China under Grant 61971129, the Natural Science Foundation of Jiangsu Province under Grant BK20181264, the National Key R&D Program of China under Grant 2018YFB1801103, the Research Fund of the State Key Laboratory of Integrated Services Networks (Xidian University) under Grant ISN19-10, and the Research Fund of the Key Laboratory of Wireless Sensor Network & Communication (Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences) under Grant 2017002.
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