Deep Reinforcement Learning for Energy-Efficient Federated Learning in UAV-Enabled Wireless Powered Networks

Autor:	Quang Vinh Do, Won-Joo Hwang, Quoc-Viet Pham
Rok vydání:	2022
Předmět:	Energy conservation Optimization problem Wireless network Computer science Modeling and Simulation Distributed computing Reinforcement learning Resource allocation Lyapunov optimization Markov decision process Electrical and Electronic Engineering Computer Science Applications Efficient energy use
Zdroj:	IEEE Communications Letters. 26:99-103
ISSN:	2373-7891 1089-7798
DOI:	10.1109/lcomm.2021.3122129
Popis:	Federated learning (FL) is a promising solution to privacy preservation for data-driven deep learning approaches. However, enabling FL in unmanned aerial vehicle (UAV)-assisted wireless networks is still challenging due to limited resources and battery capacity in the UAV and user devices. In this regard, we propose a deep reinforcement learning (DRL)-based framework for joint UAV placement and resource allocation to enable sustainable FL with energy harvesting user devices. We aim to maximize the long-term FL performance considering the limited resources in the network, such as harvested energy, bandwidth resources, and UAV’s energy budget. To reduce the complexity of the original problem, we leverage the Lyapunov optimization technique to transform a long-term energy constraint into a deterministic problem. We reformulate the optimization problem as the framework of a Markov decision process (MDP) and design a DRL-based algorithm to solve the MDP. The proposed solution can guarantee the sustainable operation of UAV-aided wireless networks by improving energy conservation of the network in the long run.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::c55bb1d472c6cddb54335c48898c0e05 https://doi.org/10.1109/lcomm.2021.3122129 Zobrazit plný text záznamu