A Two-Stage Incentive Mechanism Design for Quality Optimization of Hierarchical Federated Learning

In this paper, we investigate the aggregated model quality maximization problem in hierarchical federated learning, the decision problem of which is proved NP-complete. We develop the mechanism MaxQ to maximize the sum of local model quality, which consists of two stages. In the first stage, an algo...
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Gespeichert in:

Autor*in:	Zhuo Li [verfasserIn] Hui Du [verfasserIn] Xin Chen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Hierarchical federated learning maximization of model quality matching game contract theory incentive mechanism design

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 10(2022), Seite 132752-132762
Übergeordnetes Werk:	volume:10 ; year:2022 ; pages:132752-132762

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2022.3230695

Katalog-ID:	DOAJ01564250X

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520			\|a In this paper, we investigate the aggregated model quality maximization problem in hierarchical federated learning, the decision problem of which is proved NP-complete. We develop the mechanism MaxQ to maximize the sum of local model quality, which consists of two stages. In the first stage, an algorithm based on matching game theory is proposed to associate mobile devices with edge servers, which is proved able to achieve the stability and <inline-formula< <tex-math notation="LaTeX"<$\frac {1}{2}$ </tex-math<</inline-formula<-approximation ratio. In the second stage, we design an incentive mechanism based on contract theory to maximize the quality of models submitted by mobile devices to edge servers. Through thorough experiments, we analyse the performance of MaxQ and compare it with the existing mechanisms FAIR and EHFL, under different deep learning models ResNet18, ResNet50 and AlexNet, individually. It is found that the model quality can be improved by 8.20% and 7.81%, 10.47% and 11.87%, 10.98% and 11.97% under different models, respectively.
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allfieldsSound	10.1109/ACCESS.2022.3230695 doi (DE-627)DOAJ01564250X (DE-599)DOAJ32d7c71caad74188b636ddfa14dfbef6 DE-627 ger DE-627 rakwb eng TK1-9971 Zhuo Li verfasserin aut A Two-Stage Incentive Mechanism Design for Quality Optimization of Hierarchical Federated Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we investigate the aggregated model quality maximization problem in hierarchical federated learning, the decision problem of which is proved NP-complete. We develop the mechanism MaxQ to maximize the sum of local model quality, which consists of two stages. In the first stage, an algorithm based on matching game theory is proposed to associate mobile devices with edge servers, which is proved able to achieve the stability and <inline-formula< <tex-math notation="LaTeX"<$\frac {1}{2}$ </tex-math<</inline-formula<-approximation ratio. In the second stage, we design an incentive mechanism based on contract theory to maximize the quality of models submitted by mobile devices to edge servers. Through thorough experiments, we analyse the performance of MaxQ and compare it with the existing mechanisms FAIR and EHFL, under different deep learning models ResNet18, ResNet50 and AlexNet, individually. It is found that the model quality can be improved by 8.20% and 7.81%, 10.47% and 11.87%, 10.98% and 11.97% under different models, respectively. Hierarchical federated learning maximization of model quality matching game contract theory incentive mechanism design Electrical engineering. Electronics. Nuclear engineering Hui Du verfasserin aut Xin Chen verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 132752-132762 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:132752-132762 https://doi.org/10.1109/ACCESS.2022.3230695 kostenfrei https://doaj.org/article/32d7c71caad74188b636ddfa14dfbef6 kostenfrei https://ieeexplore.ieee.org/document/9992189/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 132752-132762
language	English
source	In IEEE Access 10(2022), Seite 132752-132762 volume:10 year:2022 pages:132752-132762
sourceStr	In IEEE Access 10(2022), Seite 132752-132762 volume:10 year:2022 pages:132752-132762
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topic_facet	Hierarchical federated learning maximization of model quality matching game contract theory incentive mechanism design Electrical engineering. Electronics. Nuclear engineering
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authorswithroles_txt_mv	Zhuo Li @@aut@@ Hui Du @@aut@@ Xin Chen @@aut@@
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callnumber-first	T - Technology
author	Zhuo Li
spellingShingle	Zhuo Li misc TK1-9971 misc Hierarchical federated learning misc maximization of model quality misc matching game misc contract theory misc incentive mechanism design misc Electrical engineering. Electronics. Nuclear engineering A Two-Stage Incentive Mechanism Design for Quality Optimization of Hierarchical Federated Learning
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topic_title	TK1-9971 A Two-Stage Incentive Mechanism Design for Quality Optimization of Hierarchical Federated Learning Hierarchical federated learning maximization of model quality matching game contract theory incentive mechanism design
topic	misc TK1-9971 misc Hierarchical federated learning misc maximization of model quality misc matching game misc contract theory misc incentive mechanism design misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Hierarchical federated learning misc maximization of model quality misc matching game misc contract theory misc incentive mechanism design misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Hierarchical federated learning misc maximization of model quality misc matching game misc contract theory misc incentive mechanism design misc Electrical engineering. Electronics. Nuclear engineering
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title	A Two-Stage Incentive Mechanism Design for Quality Optimization of Hierarchical Federated Learning
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title_full	A Two-Stage Incentive Mechanism Design for Quality Optimization of Hierarchical Federated Learning
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title_sort	two-stage incentive mechanism design for quality optimization of hierarchical federated learning
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title_auth	A Two-Stage Incentive Mechanism Design for Quality Optimization of Hierarchical Federated Learning
abstract	In this paper, we investigate the aggregated model quality maximization problem in hierarchical federated learning, the decision problem of which is proved NP-complete. We develop the mechanism MaxQ to maximize the sum of local model quality, which consists of two stages. In the first stage, an algorithm based on matching game theory is proposed to associate mobile devices with edge servers, which is proved able to achieve the stability and <inline-formula< <tex-math notation="LaTeX"<$\frac {1}{2}$ </tex-math<</inline-formula<-approximation ratio. In the second stage, we design an incentive mechanism based on contract theory to maximize the quality of models submitted by mobile devices to edge servers. Through thorough experiments, we analyse the performance of MaxQ and compare it with the existing mechanisms FAIR and EHFL, under different deep learning models ResNet18, ResNet50 and AlexNet, individually. It is found that the model quality can be improved by 8.20% and 7.81%, 10.47% and 11.87%, 10.98% and 11.97% under different models, respectively.
abstractGer	In this paper, we investigate the aggregated model quality maximization problem in hierarchical federated learning, the decision problem of which is proved NP-complete. We develop the mechanism MaxQ to maximize the sum of local model quality, which consists of two stages. In the first stage, an algorithm based on matching game theory is proposed to associate mobile devices with edge servers, which is proved able to achieve the stability and <inline-formula< <tex-math notation="LaTeX"<$\frac {1}{2}$ </tex-math<</inline-formula<-approximation ratio. In the second stage, we design an incentive mechanism based on contract theory to maximize the quality of models submitted by mobile devices to edge servers. Through thorough experiments, we analyse the performance of MaxQ and compare it with the existing mechanisms FAIR and EHFL, under different deep learning models ResNet18, ResNet50 and AlexNet, individually. It is found that the model quality can be improved by 8.20% and 7.81%, 10.47% and 11.87%, 10.98% and 11.97% under different models, respectively.
abstract_unstemmed	In this paper, we investigate the aggregated model quality maximization problem in hierarchical federated learning, the decision problem of which is proved NP-complete. We develop the mechanism MaxQ to maximize the sum of local model quality, which consists of two stages. In the first stage, an algorithm based on matching game theory is proposed to associate mobile devices with edge servers, which is proved able to achieve the stability and <inline-formula< <tex-math notation="LaTeX"<$\frac {1}{2}$ </tex-math<</inline-formula<-approximation ratio. In the second stage, we design an incentive mechanism based on contract theory to maximize the quality of models submitted by mobile devices to edge servers. Through thorough experiments, we analyse the performance of MaxQ and compare it with the existing mechanisms FAIR and EHFL, under different deep learning models ResNet18, ResNet50 and AlexNet, individually. It is found that the model quality can be improved by 8.20% and 7.81%, 10.47% and 11.87%, 10.98% and 11.97% under different models, respectively.
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title_short	A Two-Stage Incentive Mechanism Design for Quality Optimization of Hierarchical Federated Learning
url	https://doi.org/10.1109/ACCESS.2022.3230695 https://doaj.org/article/32d7c71caad74188b636ddfa14dfbef6 https://ieeexplore.ieee.org/document/9992189/ https://doaj.org/toc/2169-3536
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