首页|Privacy-preserved learning from non-i.i.d data in fog-assisted IoT:A federated learning approach

Privacy-preserved learning from non-i.i.d data in fog-assisted IoT:A federated learning approach

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With the prevalence of the Internet of Things(IoT)systems,smart cities comprise complex networks,including sensors,actuators,appliances,and cyber services.The complexity and heterogeneity of smart cities have become vulnerable to sophisticated cyber-attacks,especially privacy-related attacks such as inference and data poisoning ones.Federated Learning(FL)has been regarded as a hopeful method to enable distributed learning with privacy-preserved intelligence in IoT applications.Even though the significance of developing privacy-preserving FL has drawn as a great research interest,the current research only concentrates on FL with independent identically distributed(i.i.d)data and few studies have addressed the non-i.i.d setting.FL is known to be vulnerable to Generative Adversarial Network(GAN)attacks,where an adversary can presume to act as a contributor participating in the training process to acquire the private data of other contributors.This paper proposes an innovative Privacy Protection-based Federated Deep Learning(PP-FDL)framework,which accomplishes data protection against privacy-related GAN attacks,along with high classification rates from non-i.i.d data.PP-FDL is designed to enable fog nodes to cooperate to train the FDL model in a way that ensures contributors have no access to the data of each other,where class probabilities are protected utilizing a private identifier generated for each class.The PP-FDL framework is evaluated for image classification using simple convolutional networks which are trained using MNIST and CIFAR-10 datasets.The empirical results have revealed that PF-DFL can achieve data protection and the framework outperforms the other three state-of-the-art models with 3％-8％as accuracy improvements.

Privacy preservationFederated learningDeep learningFog computingSmart cities

Mohamed Abdel-Basset、Hossam Hawash、Nour Moustafa、Imran Razzak、Mohamed Abd Elfattah

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Faculty of Computers and Informatics,Zagazig University,Zagazig Sharqiyah,44519,Egypt

School of Engineering and Information Technology University of New South Wales@ADFA,Canberra,ACT,2600,Australia

Deakin University,Geelong Waurn Ponds Campus,Australia

Computer Science Department,Misr Higher Institute for Commerce and Computers,Mansoura,35511,Egypt

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2024

10.1016/j.dcan.2022.12.013

数字通信与网络(英文)

数字通信与网络(英文)

ISSN：
年,卷(期)：2024.10(2)
  • 43