Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/159471
Title: Joint optimization of energy consumption and completion time in federated learning
Authors: Zhou, Xinyu
Zhao, Jun
Han, Huimei
Guet, Claude
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Zhou, X., Zhao, J., Han, H. & Guet, C. (2022). Joint optimization of energy consumption and completion time in federated learning. 2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS). https://dx.doi.org/10.1109/ICDCS54860.2022.00101
Project: MOE2019-T2-1-176 
Abstract: Federated Learning (FL) is an intriguing distributed machine learning approach due to its privacy-preserving characteristics. To balance the trade-off between energy and execution latency, and thus accommodate different demands and application scenarios, we formulate an optimization problem to minimize a weighted sum of total energy consumption and completion time through two weight parameters. The optimization variables include bandwidth, transmission power and CPU frequency of each device in the FL system, where all devices are linked to a base station and train a global model collaboratively. Through decomposing the non-convex optimization problem into two subproblems, we devise a resource allocation algorithm to determine the bandwidth allocation, transmission power, and CPU frequency for each participating device. We further present the convergence analysis and computational complexity of the proposed algorithm. Numerical results show that our proposed algorithm not only has better performance at different weight parameters (i.e., different demands) but also outperforms the state of the art.
URI: https://hdl.handle.net/10356/159471
ISBN: 978-1-6654-7178-7
ISSN: 2575-8411
DOI: 10.1109/ICDCS54860.2022.00101
Rights: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ICDCS54860.2022.00101.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ERI@N Conference Papers
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