JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering

Accepted manuscript available online (unedited version)

Federated deep reinforcement learning-based computation offloading in a LEOsatellite edge computing system

Author(s): Min JIA, Jian WU, Xinyu WANG, Qing GUO
Affiliation(s): School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150006, China
Corresponding email(s): jiamin@hit.edu.cn, 20B905014@stu.hit.edu.cn, wang_xinyu@hit.edu.cn, qguo@hit.edu.cn
Key Words: Federated learning; Low earth orbit satellite; Mobile edge computing; Deep reinforcement learning; Computation offloading

Share this article to： More <<< Previous Paper \|Next Paper >>>

Min JIA, Jian WU, Xinyu WANG, Qing GUO. Federated deep reinforcement learning-based computation offloading in a LEOsatellite edge computing system[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2400448

@article{title="Federated deep reinforcement learning-based computation offloading in a LEOsatellite edge computing system",
author="Min JIA, Jian WU, Xinyu WANG, Qing GUO",
journal="Frontiers of Information Technology & Electronic Engineering",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/FITEE.2400448"
}

%0 Journal Article
%T Federated deep reinforcement learning-based computation offloading in a LEOsatellite edge computing system
%A Min JIA
%A Jian WU
%A Xinyu WANG
%A Qing GUO
%J Frontiers of Information Technology & Electronic Engineering
%P
%@ 2095-9184
%D in press
%I Zhejiang University Press & Springer
doi="https://doi.org/10.1631/FITEE.2400448"

TY - JOUR
T1 - Federated deep reinforcement learning-based computation offloading in a LEOsatellite edge computing system
A1 - Min JIA
A1 - Jian WU
A1 - Xinyu WANG
A1 - Qing GUO
J0 - Frontiers of Information Technology & Electronic Engineering
SP -
EP -
%@ 2095-9184
Y1 - in press
PB - Zhejiang University Press & Springer
ER -
doi="https://doi.org/10.1631/FITEE.2400448"

Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: Recent studies have shown that system capacity is very important for cellular networks. In this paper, we consider maximizing the weighted sum-rate of the cellular network downlink and uplink, where each cell consists of a full-duplex (FD) base station (BS) and half-duplex (HD) users. Federated learning (FL) can train models in the absence of centralized data, which can achieve privacy protection of user data. A low earth orbit (LEO) satellite edge computing system (LSECS) can be formed by placing the mobile edge computing (MEC) servers on LEO satellites, which greatly increases the processing capacities of the satellites. Therefore, this paper considers a combination of FL and MEC and proposes an FL-based computation offloading algorithm to maximize the weighted sum-rate while ensuring the security of user data. We consider solving the sub-channel assignment and power allocation problems using deep reinforcement learning (DRL) algorithms with excellent global search capabilities. The simulation results show that our proposed algorithm achieves the maximum weighted sum-rate compared with the baseline algorithms and excellent convergence.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

Open peer comments: Debate/Discuss/Question/Opinion

<1>

- Go to

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference