Content Popularity Prediction via Federated Learning in Cache-Enabled Wireless Networks

doi:10.12142/ZTECOM.202302004

ZTE Communications ›› 2023, Vol. 21 ›› Issue (2): 18-24.DOI: 10.12142/ZTECOM.202302004

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Content Popularity Prediction via Federated Learning in Cache-Enabled Wireless Networks

YAN Yuna¹, LIU Ying², NI Tao², LIN Wensheng¹, LI Lixin¹()

^1.Northwestern Polytechnical University, Xi?an 710072, China
^2.Shanghai Satellite Engineering Research Institute, Shanghai 200240, China

Received:2023-03-02 Online:2023-06-13 Published:2023-06-13
About author:YAN Yuna is currently working toward her master’s degree under the supervision of Prof. LI Lixin with the School of Electronics and Information, Northwestern Polytechnical University, China. Her research interests include federated learning, deep learning and semantic communications.|LIU Ying is an engineer of Shanghai Satellite Engineering Research Institute, China, mainly engaged in satellite system design and satellite communications.|NI Tao is a senior engineer of Shanghai Satellite Engineering Research Institute, China, mainly engaged in satellite system design and satellite communications.|LIN Wensheng received his BE degree in communication engineering and ME degree in electronic and communication engineering from Northwestern Polytechnical University, China in 2013 and 2016. He received his PhD degree in information science from the Japan Advanced Institute of Science and Technology in 2019. He is currently an associate professor with the School of Electronics and Information, Northwestern Polytechnical University. His research interests include network information theory, distributed source coding, and age of information.|LI Lixin (lilixin@nwpu.edu.cn) received his BS degree (Hons.), MS degree (Hons.), and PhD degree from Northwestern Polytechnical University (NPU), China in 2001, 2004, and 2008, respectively. From 2009 to 2011, he was a postdoctoral research fellow with NPU. In 2011, he joined the School of Electronics and Information, NPU, where he is currently a full professor and chair of Department of Communication Engineering. In 2017, he held the visiting scholar position with the University of Houston, USA. He holds 26 granted patents and has authored or coauthored five books and more than 200 peer-reviewed papers in many prestigious journals and conferences. His research interests include 5G/6G wireless networks, federated learning, game theory, and machine learning for wireless communications. He was the recipient of the 2016 NPU Outstanding Young Teacher Award, which is the highest research and education honors for young faculties in NPU. He was an exemplary reviewer for IEEE Transactions on Communications in 2020.
Supported by:
the National Natural Science Foundation of China (NSFC)(62001387);the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology (CAST)(2022QNRC001);Shanghai Academy of Spaceflight Technology (SAST)(SAST2022052)

Abstract

Abstract:

With the rapid development of networks, users are increasingly seeking richer and high-quality content experience, and there is an urgent need to develop efficient content caching strategies to improve the content distribution efficiency of caching. Therefore, it will be an effective solution to combine content popularity prediction based on machine learning (ML) and content caching to enable the network to predict and analyze popular content. However, the data sets which contain users’ private data cause the risk of privacy leakage. In this paper, to address this challenge, we propose a privacy-preserving algorithm based on federated learning (FL) and long short-term memory (LSTM), which is referred to as FL-LSTM, to predict content popularity. Simulation results demonstrate that the performance of the proposed algorithm is close to the centralized LSTM and better than other benchmark algorithms in terms of privacy protection. Meanwhile, the caching policy in this paper raises about 14.3% of the content hit rate.

Key words: content popularity prediction, privacy protection, federated learning, long short-term memory

YAN Yuna, LIU Ying, NI Tao, LIN Wensheng, LI Lixin. Content Popularity Prediction via Federated Learning in Cache-Enabled Wireless Networks[J]. ZTE Communications, 2023, 21(2): 18-24.

Figures/Tables 6

Figure 1 Scenario of a cache-enabled wireless network

Figure 2 Illustration of long short-term memory (LSTM) algorithm

Figure 3 Illustration of FL-LSTM algorithm

Figure 4 Content popularity prediction by different algorithms

Figure 5 MSE loss of LSTM and FL-LSTM algorithms

Figure 6 Total hit rates versus memory capacity

References 20

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Content Popularity Prediction via Federated Learning in Cache-Enabled Wireless Networks

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