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Smart city article details

Title	Federated Deep Reinforcement Learning-Based Task Offloading And Resource Allocation For Smart Cities In A Mobile Edge Network
ID_Doc	26323
Authors	Chen X.; Liu G.
Year	2022
Published	Sensors, 22, 13
DOI	http://dx.doi.org/10.3390/s22134738
Abstract	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. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Author Keywords	DDPG; federated learning; mobile edge computing; resource allocation; smart city; task offloading

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