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P-CA: Privacy-Preserving Convolutional Autoencoder-Based Edge–Cloud Collaborative Computing for Huma...

P-CA: Privacy-Preserving Convolutional Autoencoder-Based Edge–Cloud Collaborative Computing for Huma...

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_ba56ed43a3354e7eae430d1da680ee87

P-CA: Privacy-Preserving Convolutional Autoencoder-Based Edge–Cloud Collaborative Computing for Human Behavior Recognition

About this item

Full title

P-CA: Privacy-Preserving Convolutional Autoencoder-Based Edge–Cloud Collaborative Computing for Human Behavior Recognition

Publisher

Basel: MDPI AG

Journal title

Mathematics (Basel), 2024-08, Vol.12 (16), p.2587

Language

English

Formats

Publication information

Publisher

Basel: MDPI AG

More information

Scope and Contents

Contents

With the development of edge computing and deep learning, intelligent human behavior recognition has spawned extensive applications in smart worlds. However, current edge computing technology faces performance bottlenecks due to limited computing resources at the edge, which prevent deploying advanced deep neural networks. In addition, there is a r...

Alternative Titles

Full title

P-CA: Privacy-Preserving Convolutional Autoencoder-Based Edge–Cloud Collaborative Computing for Human Behavior Recognition

Authors, Artists and Contributors

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_doaj_primary_oai_doaj_org_article_ba56ed43a3354e7eae430d1da680ee87

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_ba56ed43a3354e7eae430d1da680ee87

Other Identifiers

ISSN

2227-7390

E-ISSN

2227-7390

DOI

10.3390/math12162587

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