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

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 r...

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Bibliographic Details
Published in:IEEE communications letters 2022-01, Vol.26 (1), p.99-103
Main Authors: Do, Quang Vinh, Pham, Quoc-Viet, Hwang, Won-Joo
Format: Article
Language:English
Subjects:
Algorithms
Bandwidth
Batteries
Deep learning
Deep reinforcement learning
Energy
Energy budget
Energy consumption
Energy harvesting
Federated learning
Machine learning
Markov processes
Optimization
Optimization techniques
Resource allocation
Task analysis
Training
unmanned aerial vehicle
Unmanned aerial vehicles
Wireless networks
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Summary: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.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2021.3122129