University of Oulu

M. Zhang, Y. Jiang, F. -C. Zheng, M. Bennis and X. You, "Cooperative Edge Caching via Federated Deep Reinforcement Learning in Fog-RANs," 2021 IEEE International Conference on Communications Workshops (ICC Workshops), 2021, pp. 1-6, doi: 10.1109/ICCWorkshops50388.2021.9473609

Cooperative Edge Caching via Federated Deep Reinforcement Learning in Fog-RANs

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Author: Zhang, Min1; Jiang, Yanxiang1,2; 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, Oulu 90014, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.3 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021102151869
Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2021-10-21
Description:

Abstract

In this paper, cooperative edge caching problem is investigated in fog radio access networks (F-RANs). By considering the non-deterministic polynomial hard (NP-hard) property of this problem, a federated deep reinforcement learning (FDRL) framework is put forth to learn the content caching strategy. Then, in order to overcome the dimensionality curse of reinforcement learning and improve the overall caching performance, we propose a dueling deep Q-network based cooperative edge caching method to find the optimal caching policy in a distributed manner. Furthermore, horizontal federated learning (HFL) is applied to address issues of over-consumption of resources during distributed training and data transmission process. Compared with three classical content caching methods and two reinforcement learning algorithms, simulation results show the superiority of our proposed method in reducing the content request delay and improving the cache hit rate.

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Series: IEEE International Conference on Communications workshop
ISSN: 2164-7038
ISSN-E: 2694-2941
ISSN-L: 2164-7038
ISBN: 978-1-7281-9441-7
ISBN Print: 978-1-7281-9442-4
Pages: 1 - 6
Article number: 9473609
DOI: 10.1109/ICCWorkshops50388.2021.9473609
OADOI: https://oadoi.org/10.1109/ICCWorkshops50388.2021.9473609
Host publication: 2021 IEEE International Conference on Communications Workshops, ICC Workshops 2021
Conference: IEEE International Conference on Communications Workshops
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 Shenzhen Science and Technology Program under Grant KQTD20190929172545139 and JCYJ20180306171815699, and the National Major Research and Development Program of China under Grant 2020YFB1805005.
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