Federated Learning for COVID-19 Detection With Generative Adversarial Networks in Edge Cloud Computing

COVID-19 has spread rapidly across the globe and become a deadly pandemic. Recently, many artificial intelligence-based approaches have been used for COVID-19 detection, but they often require public data sharing with cloud datacentres and thus remain privacy concerns. This paper proposes a new federated learning scheme, called FedGAN, to generate realistic COVID-19 images for facilitating privacy-enhanced COVID-19 detection with generative adversarial networks (GANs) in edge cloud computing. Particularly, we first propose a GAN where a discriminator and a generator based on convolutional neural networks (CNNs) at each edge-based medical institution alternatively are trained to mimic the real COVID-19 data distribution. Then, we propose a new federated learning solution which allows local GANs to collaborate and exchange learned parameters with a cloud server, aiming to enrich the global GAN model for generating realistic COVID-19 images without the need for sharing actual data. To enhance the privacy in federated COVID-19 data analytics, we integrate a differential privacy solution at each hospital institution. Moreover, we propose a new blockchain-based FedGAN framework for secure COVID-19 data analytics, by decentralizing the FL process with a new mining solution for low running latency. Simulations results demonstrate the superiority of our approach for COVID-19 detection over the state-of-the-art schemes. IEEE

[1]  Shiva Raj Pokhrel,et al.  A Blockchained Federated Learning Framework for Cognitive Computing in Industry 4.0 Networks , 2021, IEEE Transactions on Industrial Informatics.

[2]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Weishan Zhang,et al.  Dynamic-Fusion-Based Federated Learning for COVID-19 Detection , 2020, IEEE Internet of Things Journal.

[4]  Farnoosh Naderkhani,et al.  COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images , 2020, Pattern Recognition Letters.

[5]  Aruna Seneviratne,et al.  Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey , 2020, IEEE Access.

[6]  Karrar Hameed Abdulkareem,et al.  Realizing an Effective COVID-19 Diagnosis System Based on Machine Learning and IoT in Smart Hospital Environment , 2021, IEEE Internet of Things Journal.

[7]  Bradford J. Wood,et al.  Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan , 2020, Medical Image Analysis.

[8]  Won-Joo Hwang,et al.  Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts , 2020, IEEE Access.

[9]  Aruna Seneviratne,et al.  Federated Learning for Internet of Things: A Comprehensive Survey , 2021, IEEE Communications Surveys & Tutorials.

[10]  Xiaojiang Du,et al.  Exploiting Unintended Property Leakage in Blockchain-Assisted Federated Learning for Intelligent Edge Computing , 2021, IEEE Internet of Things Journal.

[11]  Ghulam Muhammad,et al.  Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach , 2020, IEEE Access.

[12]  Shaoyong Guo,et al.  Blockchain-based Asynchronous Federated Learning for Internet of Things , 2021 .

[13]  Zhengtao Yu,et al.  Trustworthy and Intelligent COVID-19 Diagnostic IoMT Through XR and Deep-Learning-Based Clinic Data Access , 2021, IEEE Internet of Things Journal.

[14]  Carlos Sáez,et al.  Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset , 2020, J. Am. Medical Informatics Assoc..

[15]  Khan Muhammad,et al.  Federated learning for COVID-19 screening from Chest X-ray images , 2021, Applied Soft Computing.

[16]  Farzad Khalvati,et al.  RANDGAN: Randomized generative adversarial network for detection of COVID-19 in chest X-ray , 2020, Scientific Reports.

[17]  Deepak Gupta,et al.  CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection , 2020, IEEE Access.

[18]  Hanseok Ko,et al.  COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Network , 2020, IEEE journal of biomedical and health informatics.

[19]  Jonathan Passerat-Palmbach,et al.  Blockchain-orchestrated machine learning for privacy preserving federated learning in electronic health data , 2020, 2020 IEEE International Conference on Blockchain (Blockchain).

[20]  Noorbakhsh Amiri Golilarz,et al.  Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging , 2021, IEEE Sensors Journal.

[21]  Yuhui Zheng,et al.  Recent Progress on Generative Adversarial Networks (GANs): A Survey , 2019, IEEE Access.

[22]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[23]  Yingxue Zhang,et al.  COVID-GAN: Estimating Human Mobility Responses to COVID-19 Pandemic through Spatio-Temporal Conditional Generative Adversarial Networks , 2020, SIGSPATIAL/GIS.

[24]  Hussein T. Mouftah,et al.  Preventing and Controlling Epidemics Through Blockchain-Assisted AI-Enabled Networks , 2021, IEEE Network.

[25]  Yifan Yang,et al.  Experiments of Federated Learning for COVID-19 Chest X-ray Images , 2020, Advances in Artificial Intelligence and Security.

[26]  Florentin Smarandache,et al.  Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning , 2020, Symmetry.

[27]  Ioannis D. Apostolopoulos,et al.  Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks , 2020, Physical and Engineering Sciences in Medicine.

[28]  Janga Vijay kumar,et al.  WITHDRAWN: Advanced machine learning-based analytics on COVID-19 data using generative adversarial networks , 2020, Materials Today: Proceedings.

[29]  Alexander J. Smola,et al.  Scaling Distributed Machine Learning with the Parameter Server , 2014, OSDI.

[30]  Yuedong Yang,et al.  Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[31]  U. Rajendra Acharya,et al.  Automated detection of COVID-19 cases using deep neural networks with X-ray images , 2020, Computers in Biology and Medicine.

[32]  Aruna Seneviratne,et al.  BEdgeHealth: A Decentralized Architecture for Edge-Based IoMT Networks Using Blockchain , 2021, IEEE Internet of Things Journal.

[33]  D. Rueckert,et al.  Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study , 2021, npj Digital Medicine.

[34]  Zhipeng Cai,et al.  Collaborative City Digital Twin For Covid-19 Pandemic: A Federated Learning Solution , 2020, ArXiv.

[35]  H. Vincent Poor,et al.  Federated Learning for Industrial Internet of Things in Future Industries , 2021, IEEE Wireless Communications.

[36]  H. Vincent Poor,et al.  Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges , 2021, IEEE Internet of Things Journal.

[37]  Dong In Kim,et al.  Toward Secure Blockchain-Enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theory , 2018, IEEE Transactions on Vehicular Technology.

[38]  Xiangjie Kong,et al.  Real-Time Mask Identification for COVID-19: An Edge-Computing-Based Deep Learning Framework , 2021, IEEE Internet of Things Journal.

[39]  Tong Zhou,et al.  DLattice: A Permission-Less Blockchain Based on DPoS-BA-DAG Consensus for Data Tokenization , 2019, IEEE Access.

[40]  Nour Eldeen M. Khalifa,et al.  Detection of Coronavirus (COVID-19) Associated Pneumonia based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model using Chest X-ray Dataset , 2020, AISI.