FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia

Abstract

Existing deep learning technologies generally learn the features of chest X-ray data generated by Generative Adversarial Networks (GAN) to diagnose COVID-19 pneumonia. However, the above methods have a critical challenge: data privacy. GAN will leak the semantic information of the training data which can be used to reconstruct the training samples by attackers, thereby this method will leak the privacy of the patient. Furthermore, for this reason that is the limitation of the training data sample, different hospitals jointly train the model through data sharing, which will also cause the privacy leakage. To solve this problem, we adopt the Federated Learning (FL) frame-work which is a new technique being used to protect the data privacy. Under the FL framework and Differentially Private thinking, we propose a FederatedDifferentially Private Generative Adversarial Network (FedDPGAN) to detectCOVID-19 pneumonia for sustainable smart cities. Specifically, we use DP-GAN to privately generate diverse patient data in which differential privacy technology is introduced to make sure the privacy protection of the semantic information of training dataset. Furthermore, we leverage FL to allow hospitals to collaboratively train COVID-19 models without sharing the original data. Under Independent and Identically Distributed (IID) and non-IID settings, The evaluation of the proposed model is on three types of chest X-ray (CXR) images dataset (COVID-19, normal, and normal pneumonia). A large number of the truthful reports make the verification of our model can effectively diagnose COVID-19 without compromising privacy.

References in corpus (9)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
• COVID-19 Image Data Collection: Prospective Predictions Are the Future
• CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection
• Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach
• Federated Learning for 6G Communications: Challenges, Methods, and Future Directions
• A Secure Federated Learning Framework for 5G Networks
• Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms
• Towards Communication-efficient and Attack-Resistant Federated Edge Learning for Industrial Internet of Things

Cited by in corpus (3)

• Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review
• Federated Domain Generalization: A Survey
• Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Review