Title
Federated Learning for Online Resource Allocation in Mobile Edge Computing: A Deep Reinforcement Learning Approach
Author
Zheng, Jingjing (CISTER Research Centre)
Li, Kai (CISTER Research Centre)
Mhaisen, N. (TU Delft Networked Systems)
Ni, Wei (CSIRO: Commonwealth Scientific and Industrial Research)
Tovar, Eduardo (CISTER Research Centre)
Guizani, Mohsen (Mohamed Bin Zayed University of Artificial Intelligence)
Date
2023
Abstract
Federated learning (FL) is increasingly considered to circumvent the disclosure of private data in mobile edge computing (MEC) systems. Training with large data can enhance FL learning accuracy, which is associated with non-negligible energy use. Scheduled edge devices with small data save energy but decrease FL learning accuracy due to a reduction in energy consumption. A trade-off between the energy consumption of edge devices and the learning accuracy of FL is formulated in this proposed work. The FL-enabled twin-delayed deep deterministic policy gradient (FL-TD3) framework is proposed as a solution to the formulated problem because its state and action spaces are large in a continuous domain. This framework provides the maximum accuracy ratio of FL divided by the device’s energy consumption. A comparison of the numerical results with the state-of-the-art demonstrates that the ratio has been improved significantly.
Subject
Federated learning
mobile edge computing
online resource allocation
deep reinforcement learning
To reference this document use:
http://resolver.tudelft.nl/uuid:021b6617-c9bd-4b61-9a93-18f43294fe2e
DOI
https://doi.org/10.1109/WCNC55385.2023.10118940
Publisher
IEEE, Piscataway
Embargo date
2023-11-12
ISBN
978-1-6654-9123-5
Source
Proceedings of the 2023 IEEE Wireless Communications and Networking Conference (WCNC)
Event
2023 IEEE Wireless Communications and Networking Conference (WCNC), 2023-03-26 → 2023-03-29, Glasgow, United Kingdom
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Part of collection
Institutional Repository
Document type
conference paper
Rights
© 2023 Jingjing Zheng, Kai Li, N. Mhaisen, Wei Ni, Eduardo Tovar, Mohsen Guizani