Distributing DNN training over IoT edge devices based on transfer learning.
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| Title: | Distributing DNN training over IoT edge devices based on transfer learning. |
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| Authors: | Tanghatari, Ehsan1 (AUTHOR) tanghatari.e@ut.ac.ir, Kamal, Mehdi1 (AUTHOR) mehdikamal@ut.ac.ir, Afzali-Kusha, Ali1 (AUTHOR) afzali@ut.ac.ir, Pedram, Massoud2 (AUTHOR) pedram@usc.edu |
| Source: | Neurocomputing. Jan2022, Vol. 467, p56-65. 10p. |
| Subjects: | Internet of things, Cloud storage, Distillation |
| Abstract: | In this paper, an approach for distributing the deep neural network (DNN) training onto IoT edge devices is proposed. The approach results in protecting data privacy on the edge devices and decreasing the load on cloud servers. In addition, the technique may reduce the communication traffic between the cloud and the edge devices. Since the available resources in the edge devices are limited, in the proposed approach, we suggest a heuristic technique for generating a smaller network based on the main network in cloud. Next, by exploiting the knowledge distillation method, the knowledge of the main network is transferred to the generated small network. In this approach, small networks on the edge devices under different datasets are trained where some of their parameters are aggregated for updating the main network parameters on the cloud. The effectiveness of this approach is assessed with some state-of-the-art neural networks. Results show that the approach, the price of preserving the data privacy, is, on average, about 3.5% accuracy loss compared to the case when the network is trained on the cloud and all the datasets of the edge devices are available for training. [ABSTRACT FROM AUTHOR] |
| Copyright of Neurocomputing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 153322539 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Distributing DNN training over IoT edge devices based on transfer learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Tanghatari%2C+Ehsan%22">Tanghatari, Ehsan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tanghatari.e@ut.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Kamal%2C+Mehdi%22">Kamal, Mehdi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mehdikamal@ut.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Afzali-Kusha%2C+Ali%22">Afzali-Kusha, Ali</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> afzali@ut.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Pedram%2C+Massoud%22">Pedram, Massoud</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> pedram@usc.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neurocomputing%22">Neurocomputing</searchLink>. Jan2022, Vol. 467, p56-65. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Internet+of+things%22">Internet of things</searchLink><br /><searchLink fieldCode="DE" term="%22Cloud+storage%22">Cloud storage</searchLink><br /><searchLink fieldCode="DE" term="%22Distillation%22">Distillation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this paper, an approach for distributing the deep neural network (DNN) training onto IoT edge devices is proposed. The approach results in protecting data privacy on the edge devices and decreasing the load on cloud servers. In addition, the technique may reduce the communication traffic between the cloud and the edge devices. Since the available resources in the edge devices are limited, in the proposed approach, we suggest a heuristic technique for generating a smaller network based on the main network in cloud. Next, by exploiting the knowledge distillation method, the knowledge of the main network is transferred to the generated small network. In this approach, small networks on the edge devices under different datasets are trained where some of their parameters are aggregated for updating the main network parameters on the cloud. The effectiveness of this approach is assessed with some state-of-the-art neural networks. Results show that the approach, the price of preserving the data privacy, is, on average, about 3.5% accuracy loss compared to the case when the network is trained on the cloud and all the datasets of the edge devices are available for training. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neurocomputing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.neucom.2021.09.045 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 56 Subjects: – SubjectFull: Internet of things Type: general – SubjectFull: Cloud storage Type: general – SubjectFull: Distillation Type: general Titles: – TitleFull: Distributing DNN training over IoT edge devices based on transfer learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Tanghatari, Ehsan – PersonEntity: Name: NameFull: Kamal, Mehdi – PersonEntity: Name: NameFull: Afzali-Kusha, Ali – PersonEntity: Name: NameFull: Pedram, Massoud IsPartOfRelationships: – BibEntity: Dates: – D: 07 M: 01 Text: Jan2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 09252312 Numbering: – Type: volume Value: 467 Titles: – TitleFull: Neurocomputing Type: main |
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