FedEco: Achieving Energy-Efficient Federated Learning by Hyperparameter Adaptive Tuning

Liu, Y; Zhang, X; Zeng, Z; Yu, S

FedEco: Achieving Energy-Efficient Federated Learning by Hyperparameter Adaptive Tuning

Liu, Y Zhang, X Zeng, Z Yu, S

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Cognitive Communications and Networking, 2024, PP, (99), pp. 1-1
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Liu, Y
dc.contributor.author	Zhang, X
dc.contributor.author	Zeng, Z
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2024-08-21T05:55:59Z
dc.date.available	2024-08-21T05:55:59Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Transactions on Cognitive Communications and Networking, 2024, PP, (99), pp. 1-1
dc.identifier.issn	2372-2045
dc.identifier.issn	2332-7731
dc.identifier.uri	http://hdl.handle.net/10453/180530
dc.description.abstract	In Federated Learning (FL), each participating client needs to frequently compute the local gradient updates and communicate with the central parameter server, which causes high energy consumption on clients. Such energy consumption brings a significant challenge to battery-constrained clients (such as mobile devices, notebooks, IoT sensors, etc.) for edge networks. To tackle this challenge, in this study, we propose a method named FedEco to minimize energy consumption of all participating clients in FL. FedEco has the following features: i). FedEco achieves energy consumption minimization through hyperparameter1 adaptive tuning, which means allocating several kinds of optimized hyperparameters to the FL clients periodically. ii). The used hyperparameters include computing hyperparameters (i.e., training speed and training volume) and communication hyperparameters (i.e., transmission speed and transmission volume). iii). FedEco can theoretically guarantee FL convergence while satisfying the heterogeneous and dynamic edge environments. iv). FedEco finds the optimal computing and communication hyperparameters by decomposing the studied problem into several subproblems and solving them iteratively. Therefore, FedEco can achieve a good tradeoff between energy efficiency and model training convergence. We conduct extensive simulations to show that FedEco significantly saves energy consumption by up to 87.04% compared with the benchmark algorithms on average while guaranteeing the convergence of model training.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Cognitive Communications and Networking
dc.relation.isbasedon	10.1109/TCCN.2024.3408416
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4604 Cybersecurity and privacy
dc.subject.classification	4606 Distributed computing and systems software
dc.title	FedEco: Achieving Energy-Efficient Federated Learning by Hyperparameter Adaptive Tuning
dc.type	Journal Article
utslib.citation.volume	PP
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/Strength - CCSP - Centre for Cyber Security and Privacy
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Centre for Cyber Security and Privacy (CCSP)
utslib.copyright.status	closed_access	*
dc.date.updated	2024-08-21T05:55:55Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

In Federated Learning (FL), each participating client needs to frequently compute the local gradient updates and communicate with the central parameter server, which causes high energy consumption on clients. Such energy consumption brings a significant challenge to battery-constrained clients (such as mobile devices, notebooks, IoT sensors, etc.) for edge networks. To tackle this challenge, in this study, we propose a method named FedEco to minimize energy consumption of all participating clients in FL. FedEco has the following features: i). FedEco achieves energy consumption minimization through hyperparameter1 adaptive tuning, which means allocating several kinds of optimized hyperparameters to the FL clients periodically. ii). The used hyperparameters include computing hyperparameters (i.e., training speed and training volume) and communication hyperparameters (i.e., transmission speed and transmission volume). iii). FedEco can theoretically guarantee FL convergence while satisfying the heterogeneous and dynamic edge environments. iv). FedEco finds the optimal computing and communication hyperparameters by decomposing the studied problem into several subproblems and solving them iteratively. Therefore, FedEco can achieve a good tradeoff between energy efficiency and model training convergence. We conduct extensive simulations to show that FedEco significantly saves energy consumption by up to 87.04% compared with the benchmark algorithms on average while guaranteeing the convergence of model training.

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http://hdl.handle.net/10453/180530