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

作　　者：Mohamed Abdel-Basset Hossam Hawash Nour Moustafa Imran Razzak Mohamed Abd Elfattah 

机构地区：[1]Faculty of Computers and Informatics,Zagazig University,Zagazig,Sharqiyah,44519,Egypt [2]School of Engineering and Information Technology,University of New South Wales@ADFA,Canberra,ACT,2600,Australia [3]Deakin University,Geelong Waurn Ponds Campus,Australia [4]Computer Science Department,Misr Higher Institute for Commerce and Computers,Mansoura,35511,Egypt

出　　处：《Digital Communications and Networks》2024年第2期404-415,共12页数字通信与网络（英文版）

摘　　要：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 privacypreserved 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.

关 键 词：Privacy preservation Federated learning Deep learning Fog computing Smart cities 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]