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Towards energy-aware federated learning on battery-powered clients

Authors:

Ahmed M. AbdelmoniemAuthors Info & Claims

FedEdge '22: Proceedings of the 1st ACM Workshop on Data Privacy and Federated Learning Technologies for Mobile Edge Network

Pages 7 - 12

https://doi.org/10.1145/3556557.3557952

Published: 22 November 2022 Publication History

Abstract

Federated learning (FL) is a newly emerged branch of AI that facilitates edge devices to collaboratively train a global machine learning model without centralizing data and with privacy by default. However, despite the remarkable advancement, this paradigm comes with various challenges. Specifically, in large-scale deployments, client heterogeneity is the norm which impacts training quality such as accuracy, fairness, and time. Moreover, energy consumption across these battery-constrained devices is largely unexplored and a limitation for wide-adoption of FL. To address this issue, we develop EAFL, an energy-aware FL selection method that considers energy consumption to maximize the participation of heterogeneous target devices. EAFL is a power-aware training algorithm that cherry-picks clients with higher battery levels in conjunction with its ability to maximize the system efficiency. Our design jointly minimizes the time-to-accuracy and maximizes the remaining on-device battery levels. EAFL improves the testing model accuracy by up to 85% and decreases the drop-out of clients by up to 2.45X.¹

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Cited By

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cover image ACM Conferences

FedEdge '22: Proceedings of the 1st ACM Workshop on Data Privacy and Federated Learning Technologies for Mobile Edge Network

October 2022

34 pages

ISBN:9781450395212

DOI:10.1145/3556557

Copyright © 2022 ACM.

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Published: 22 November 2022

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ACM MobiCom '22: The 28th Annual International Conference on Mobile Computing and Networking

October 17, 2022

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Cited By

Elshair IKhanzada TShahid MSiddiqui S(2024)Evaluating Federated Learning Simulators: A Comparative Analysis of Horizontal and Vertical ApproachesSensors10.3390/s2416514924:16(5149)Online publication date: 9-Aug-2024
https://doi.org/10.3390/s24165149
Yan KShu NWu TLiu CYang P(2024)A survey of energy-efficient strategies for federated learning inmobile edge computing移动边缘计算中联邦学习的能效策略综述Frontiers of Information Technology & Electronic Engineering10.1631/FITEE.230018125:5(645-663)Online publication date: 7-Jun-2024
https://doi.org/10.1631/FITEE.2300181
Jing JYang YLi MLi BZhang JMa J(2024)Blockchain-based federal learning program for drone safetyInternational Conference on Computer Network Security and Software Engineering (CNSSE 2024)10.1117/12.3031895(8)Online publication date: 6-Jun-2024
https://doi.org/10.1117/12.3031895
Arouj AAbdelmoniem AAlhilal AYou LWang C(2024)Resource-Aware Split Federated Learning for Edge Intelligence2024 IEEE 3rd Workshop on Machine Learning on Edge in Sensor Systems (SenSys-ML)10.1109/SenSys-ML62579.2024.00008(15-20)Online publication date: 13-May-2024
https://doi.org/10.1109/SenSys-ML62579.2024.00008
Esquivel JAldous BAbdelmoniem AAlhilal AYou L(2024)Performance Profiling of Federated Learning Across Heterogeneous Mobile Devices2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C)10.1109/QRS-C63300.2024.00053(363-372)Online publication date: 1-Jul-2024
https://doi.org/10.1109/QRS-C63300.2024.00053
Zhang HWu TCheng SLiu J(2024)CC-FedAvg: Computationally Customized Federated AveragingIEEE Internet of Things Journal10.1109/JIOT.2023.330008011:3(4826-4841)Online publication date: 1-Feb-2024
https://doi.org/10.1109/JIOT.2023.3300080
Lee JSolat FKim TPoor H(2024)Federated Learning-Empowered Mobile Network Management for 5G and Beyond Networks: From Access to CoreIEEE Communications Surveys & Tutorials10.1109/COMST.2024.335291026:3(2176-2212)Online publication date: Nov-2025
https://doi.org/10.1109/COMST.2024.3352910
Puppala SHossain IAlam MTalukder ZBahauddin STalukder S(2024)SCALE: Self-Regulated Clustered FederAted LEarning in a Homogeneous Environment2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC61105.2024.00112(801-806)Online publication date: 2-Jul-2024
https://doi.org/10.1109/COMPSAC61105.2024.00112
Maciel Fde Souza ABittencourt LVillas LBraun T(2024)Federated learning energy saving through client selectionPervasive and Mobile Computing10.1016/j.pmcj.2024.101948103:COnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.pmcj.2024.101948
Wen TZhang HZhang HWu HWang DLiu XZhang WWang YCao S(2024)RTIFedComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2024.110192241:COnline publication date: 25-Jun-2024
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