Privacy-aware offloading for training tasks of generative adversarial network in edge computing

Xu, X; Liu, X; Yin, X; Wang, S; Qi, Q; Qi, L

Privacy-aware offloading for training tasks of generative adversarial network in edge computing

Xu, X Liu, X Yin, X Wang, S

Qi, Q Qi, L

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Information Sciences, 2020, 532, pp. 1-15
Issue Date:: 2020-09-01

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Field	Value	Language
dc.contributor.author	Xu, X
dc.contributor.author	Liu, X
dc.contributor.author	Yin, X
dc.contributor.author	Wang, S https://orcid.org/0000-0003-1133-9379
dc.contributor.author	Qi, Q
dc.contributor.author	Qi, L
dc.date.accessioned	2021-03-28T21:28:43Z
dc.date.available	2021-03-28T21:28:43Z
dc.date.issued	2020-09-01
dc.identifier.citation	Information Sciences, 2020, 532, pp. 1-15
dc.identifier.issn	0020-0255
dc.identifier.uri	http://hdl.handle.net/10453/147581
dc.description.abstract	© 2020 Elsevier Inc. Currently, the generative adversarial network (GAN), with complex training processes in the physical machine (PM), has achieved great priority in image generation, audio conversion, image translation, etc. To improve the training efficiency of GAN, the edge computing paradigm is accepted as an alternative of the PMs to accommodate the training tasks, that is, the training tasks are migrated to the edge nodes (ENs) for hosting. However, it is still a key challenge to keep the overall network performance (i.e., load balance, transmission time) and privacy protection of training tasks at the same time. To address this challenge, a privacy-aware task offloading method, named POM, is developed accordingly in this paper. First, improving the strength pareto evolutionary algorithm (SPEA2) is fully investigated to obtain the offloading strategies for collaboratively improving the training performance and privacy preservation. Then, the most balanced offloading strategy is acquired for training GAN. Eventually, systematic experiments indicate that POM achieves an optimal performance efficiently among the other representative benchmark methods.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Information Sciences
dc.relation.isbasedon	10.1016/j.ins.2020.04.026
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Privacy-aware offloading for training tasks of generative adversarial network in edge computing
dc.type	Journal Article
utslib.citation.volume	532
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2021-03-28T21:28:41Z
pubs.publication-status	Published
pubs.volume	532

Abstract:

© 2020 Elsevier Inc. Currently, the generative adversarial network (GAN), with complex training processes in the physical machine (PM), has achieved great priority in image generation, audio conversion, image translation, etc. To improve the training efficiency of GAN, the edge computing paradigm is accepted as an alternative of the PMs to accommodate the training tasks, that is, the training tasks are migrated to the edge nodes (ENs) for hosting. However, it is still a key challenge to keep the overall network performance (i.e., load balance, transmission time) and privacy protection of training tasks at the same time. To address this challenge, a privacy-aware task offloading method, named POM, is developed accordingly in this paper. First, improving the strength pareto evolutionary algorithm (SPEA2) is fully investigated to obtain the offloading strategies for collaboratively improving the training performance and privacy preservation. Then, the most balanced offloading strategy is acquired for training GAN. Eventually, systematic experiments indicate that POM achieves an optimal performance efficiently among the other representative benchmark methods.

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http://hdl.handle.net/10453/147581