Privacy-Preserving Ensemble Infused Enhanced Deep Neural Network Framework for Edge Cloud Convergence Article Swipe

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Veronika Stephanie , Ibrahim Khalil , Mohammad Saidur Rahman , Mohammed Atiquzzaman ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.1109/jiot.2022.3151982

We propose a privacy-preserving ensemble infused enhanced Deep Neural Network\n(DNN) based learning framework in this paper for Internet-of-Things (IoT),\nedge, and cloud convergence in the context of healthcare. In the convergence,\nedge server is used for both storing IoT produced bioimage and hosting DNN\nalgorithm for local model training. The cloud is used for ensembling local\nmodels. The DNN-based training process of a model with a local dataset suffers\nfrom low accuracy, which can be improved by the aforementioned convergence and\nEnsemble Learning. The ensemble learning allows multiple participants to\noutsource their local model for producing a generalized final model with high\naccuracy. Nevertheless, Ensemble Learning elevates the risk of leaking\nsensitive private data from the final model. The proposed framework presents a\nDifferential Privacy-based privacy-preserving DNN with Transfer Learning for a\nlocal model generation to ensure minimal loss and higher efficiency at edge\nserver. We conduct several experiments to evaluate the performance of our\nproposed framework.\n

Related Topics

Computer Science

Enhanced Data Rates For Gsm Evolution

Artificial Intelligence

Concepts

Computer science Cloud computing Differential privacy Convergence (economics) Enhanced Data Rates for GSM Evolution Context (archaeology) Artificial intelligence Artificial neural network Ensemble forecasting Edge device Machine learning Deep learning Ensemble learning Process (computing) Data mining Economic growth Biology Operating system Economics Paleontology

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Type: article
Language: en
Landing Page: https://doi.org/10.1109/jiot.2022.3151982
OA Status: green
Cited By: 19
References: 46
Related Works: 10
OpenAlex ID: https://openalex.org/W4213336077

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DOI: https://doi.org/10.1109/jiot.2022.3151982

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Title: Privacy-Preserving Ensemble Infused Enhanced Deep Neural Network Framework for Edge Cloud Convergence

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Language: en

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Publication year: 2022

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Publication date: 2022-02-16

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Authors: Veronika Stephanie, Ibrahim Khalil, Mohammad Saidur Rahman, Mohammed Atiquzzaman

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Landing page: https://doi.org/10.1109/jiot.2022.3151982

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Concepts: Computer science, Cloud computing, Differential privacy, Convergence (economics), Enhanced Data Rates for GSM Evolution, Context (archaeology), Artificial intelligence, Artificial neural network, Ensemble forecasting, Edge device, Machine learning, Deep learning, Ensemble learning, Process (computing), Data mining, Economic growth, Biology, Operating system, Economics, Paleontology

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Cited by: 19

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