Next Article in Journal
Calibration of PurpleAir PA-I and PA-II Monitors Using Daily Mean PM2.5 Concentrations Measured in California, Washington, and Oregon from 2017 to 2021
Previous Article in Journal
Towards Estimating Arterial Diameter Using Bioimpedance Spectroscopy: A Computational Simulation and Tissue Phantom Analysis
Article

Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network

School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, China
*
Author to whom correspondence should be addressed.
Academic Editor: Antonio Cano-Ortega
Sensors 2022, 22(13), 4738; https://doi.org/10.3390/s22134738
Received: 26 May 2022 / Revised: 16 June 2022 / Accepted: 17 June 2022 / Published: 23 June 2022
(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)
Mobile edge computing (MEC) has become an indispensable part of the era of the intelligent manufacturing industry 4.0. In the smart city, computation-intensive tasks can be offloaded to the MEC server or the central cloud server for execution. However, the privacy disclosure issue may arise when the raw data is migrated to other MEC servers or the central cloud server. Since federated learning has the characteristics of protecting the privacy and improving training performance, it is introduced to solve the issue. In this article, we formulate the joint optimization problem of task offloading and resource allocation to minimize the energy consumption of all Internet of Things (IoT) devices subject to delay threshold and limited resources. A two-timescale federated deep reinforcement learning algorithm based on Deep Deterministic Policy Gradient (DDPG) framework (FL-DDPG) is proposed. Simulation results show that the proposed algorithm can greatly reduce the energy consumption of all IoT devices. View Full-Text
Keywords: smart city; mobile edge computing; task offloading; resource allocation; DDPG; federated learning smart city; mobile edge computing; task offloading; resource allocation; DDPG; federated learning
Show Figures

Figure 1

MDPI and ACS Style

Chen, X.; Liu, G. Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network. Sensors 2022, 22, 4738. https://doi.org/10.3390/s22134738

AMA Style

Chen X, Liu G. Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network. Sensors. 2022; 22(13):4738. https://doi.org/10.3390/s22134738

Chicago/Turabian Style

Chen, Xing, and Guizhong Liu. 2022. "Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network" Sensors 22, no. 13: 4738. https://doi.org/10.3390/s22134738

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop