mlsanityData to 7 Oct 2026

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

arXiv 2104.12581 ↗First posted 26 Apr 2021eess.IV cs.CV cs.LGcited by 22

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 26 of its 72 references that are in the map. First posted on arXiv 26 Apr 2021.

Builds on

26 of 72 references.

References · 26

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  4. 2016Deep Learning with Differential Privacycited by 2,682
  5. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  6. 2017Renyi Differential Privacycited by 643
  7. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  8. 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 326
  9. 2020COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Imagescited by 264
  10. 2018Differentially Private Generative Adversarial Networkcited by 235
  11. 2017Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?cited by 220
  12. 2017Generating Multi-label Discrete Patient Records using Generative Adversarial Networkscited by 197
14 more
  1. 2019Image Processing Using Multi-Code GAN Priorcited by 185
  2. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  3. 2020COVID-19 Image Data Collectioncited by 100
  4. 2020COVID-19 Image Data Collection: Prospective Predictions Are the Futurecited by 100
  5. 2020Privacy-preserving Traffic Flow Prediction: A Federated Learning Approachcited by 82
  6. 2021CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detectioncited by 64
  7. 2020Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approachcited by 56
  8. 2020Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Datacited by 38
  9. 2019PPGAN: Privacy-preserving Generative Adversarial Networkcited by 34
  10. 2020A Secure Federated Learning Framework for 5G Networkscited by 32
  11. 2020Federated Learning for 6G Communications: Challenges, Methods, and Future Directionscited by 32
  12. 2020Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarmscited by 26
  13. 2020Towards Communication-efficient and Attack-Resistant Federated Edge Learning for Industrial Internet of Thingscited by 9
  14. 2019Dominant Dataset Selection Algorithms for Electricity Consumption Time-Series Data Analysis Based on Affine Transformationcited by 1

Built on by

Cited by year

Citing papers per year: 2022: 7; 2023: 10; 2024: 4; 2025: 1.

22 papers.

Sort:

By area:

  1. 2023Differentially Private Diffusion Models Generate Useful Synthetic Imagescited by 85
  2. 2022Federated Learning with GAN-based Data Synthesis for Non-IID Clientscited by 25
  3. 2023Federated Domain Generalization: A Surveycited by 25
  4. 2022One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesiscited by 22
  5. 2023A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiencycited by 21
  6. 2022DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharingcited by 17
  7. 2022DPD-fVAE: Synthetic Data Generation Using Federated Variational Autoencoders With Differentially-Private Decodercited by 14
  8. 2023Feature Matching Data Synthesis for Non-IID Federated Learningcited by 13
  9. 2023FedCiR: Client-Invariant Representation Learning for Federated Non-IID Featurescited by 12
  10. 2024ECLIPSE: Expunging Clean-label Indiscriminate Poisons via Sparse Diffusion Purificationcited by 9
  11. 2022BOBA: Byzantine-Robust Federated Learning with Label Skewnesscited by 6
  12. 2025Subgraph Federated Learning for Local Generalizationcited by 6
10 more
  1. 2024A Systematic Review of Federated Generative Modelscited by 5
  2. 2022Privacy in Practice: Private COVID-19 Detection in X-Ray Images (Extended Version)cited by 4
  3. 2023A Unified View of Differentially Private Deep Generative Modelingcited by 4
  4. 2024Sentinel-Guided Zero-Shot Learning: A Collaborative Paradigm without Real Data Exposurecited by 4
  5. 2022Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Reviewcited by 3
  6. 2023Leveraging GANs for data scarcity of COVID-19: Beyond the hypecited by 3
  7. 2023Adversarial Robustness in Unsupervised Machine Learning: A Systematic Reviewcited by 3
  8. 2023Personalized Privacy-Preserving Framework for Cross-Silo Federated Learningcited by 2
  9. 2024Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesiscited by 2
  10. 2023FedWOA: A Federated Learning Model that uses the Whale Optimization Algorithm for Renewable Energy Predictioncited by 1