mlsanityData to 7 Oct 2026

Experiments of Federated Learning for COVID-19 Chest X-ray Images

arXiv 2007.05592 ↗First posted 5 Jul 2020eess.IV cs.CV cs.LGcited by 31

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Abstract (arXiv)

AI plays an important role in COVID-19 identification. Computer vision and deep learning techniques can assist in determining COVID-19 infection with Chest X-ray Images. However, for the protection and respect of the privacy of patients, the hospital's specific medical-related data did not allow leakage and sharing without permission. Collecting such training data was a major challenge. To a certain extent, this has caused a lack of sufficient data samples when performing deep learning approaches to detect COVID-19. Federated Learning is an available way to address this issue. It can effectively address the issue of data silos and get a shared model without obtaining local data. In the work, we propose the use of federated learning for COVID-19 data training and deploy experiments to verify the effectiveness. And we also compare performances of four popular models (MobileNet, ResNet18, MoblieNet, and COVID-Net) with the federated learning framework and without the framework. This work aims to inspire more researches on federated learning about COVID-19.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jul 2020.

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Citing papers per year: 2021: 13; 2022: 5; 2023: 3; 2024: 10.

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  1. 2021Federated Learning for Smart Healthcare: A Surveycited by 182
  2. 2021Federated Learning for Internet of Things: Applications, Challenges, and Opportunitiescited by 49
  3. 2021FLOP: Federated Learning on Medical Datasets using Partial Networkscited by 42
  4. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 34
  5. 2022Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Surveycited by 31
  6. 2021Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Diseasecited by 19
  7. 2021Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challengescited by 17
  8. 2021Federated Learning for Industrial Internet of Things in Future Industriescited by 14
  9. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
  10. 2024Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentationcited by 8
  11. 2023Advancements in Federated Learning: Models, Methods, and Privacycited by 7
  12. 2021Blockchain-based Trustworthy Federated Learning Architecturecited by 5
19 more
  1. 2021Federated Learning: Issues in Medical Applicationcited by 4
  2. 2024A Federated Learning Approach to Privacy Preserving Offensive Language Identificationcited by 4
  3. 2022A Two-Stage Federated Transfer Learning Framework in Medical Images Classification on Limited Data: A COVID-19 Case Studycited by 2
  4. 2023Federated attention consistent learning models for prostate cancer diagnosis and Gleason gradingcited by 2
  5. 2024On the Convergence Rates of Federated Q-Learning across Heterogeneous Environmentscited by 2
  6. 2024Blockchain Meets LLMs: A Living Survey on Bidirectional Integrationcited by 2
  7. 2021Systematic investigation into generalization of COVID-19 CT deep learning models with Gabor ensemble for lung involvement scoringcited by 1
  8. 2021A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imagingcited by 1
  9. 2021DPCOVID: Privacy-Preserving Federated Covid-19 Detectioncited by 1
  10. 2022Application of Federated Learning in Building a Robust COVID-19 Chest X-ray Classification Modelcited by 1
  11. 2023Fed-Safe: Securing Federated Learning in Healthcare Against Adversarial Attackscited by 1
  12. 2024A Multi-Level Approach for Class Imbalance Problem in Federated Learning for Remote Industry 4.0 Applicationscited by 1
  13. 2024In-depth Analysis of Privacy Threats in Federated Learning for Medical Datacited by 1
  14. 2021A Novel Optimized Asynchronous Federated Learning Frameworkcited by 0
  15. 2022Privacy-Preserving Online Content Moderation: A Federated Learning Use Casecited by 0
  16. 2024Bayesian Networks and Machine Learning for COVID-19 Severity Explanation and Demographic Symptom Classificationcited by 0
  17. 2024MV-Adapter: Enhancing Underwater Instance Segmentation via Adaptive Channel Attentioncited by 0
  18. 2024EPIC: Enhancing Privacy through Iterative Collaborationcited by 0
  19. 2024Artificial Intelligence in Pediatric Echocardiography: Exploring Challenges, Opportunities, and Clinical Applications with Explainable AI and Federated Learningcited by 0