mlsanityData to 7 Oct 2026

Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging

arXiv 2007.06537 ↗First posted 10 Jul 2020eess.IV cs.CV cs.LGcited by 56

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Abstract (arXiv)

With the increase of COVID-19 cases worldwide, an effective way is required to diagnose COVID-19 patients. The primary problem in diagnosing COVID-19 patients is the shortage and reliability of testing kits, due to the quick spread of the virus, medical practitioners are facing difficulty identifying the positive cases. The second real-world problem is to share the data among the hospitals globally while keeping in view the privacy concerns of the organizations. Building a collaborative model and preserving privacy are major concerns for training a global deep learning model. This paper proposes a framework that collects a small amount of data from different sources (various hospitals) and trains a global deep learning model using blockchain based federated learning. Blockchain technology authenticates the data and federated learning trains the model globally while preserving the privacy of the organization. First, we propose a data normalization technique that deals with the heterogeneity of data as the data is gathered from different hospitals having different kinds of CT scanners. Secondly, we use Capsule Network-based segmentation and classification to detect COVID-19 patients. Thirdly, we design a method that can collaboratively train a global model using blockchain technology with federated learning while preserving privacy. Additionally, we collected real-life COVID-19 patients data, which is, open to the research community. The proposed framework can utilize up-to-date data which improves the recognition of computed tomography (CT) images. Finally, our results demonstrate a better performance to detect COVID-19 patients.

Key facts. Cited by 56 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jul 2020.

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Citing papers per year: 2021: 20; 2022: 13; 2023: 10; 2024: 11; 2025: 2.

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  1. 2021Model-Contrastive Federated Learningcited by 762
  2. 2021Federated Learning for Smart Healthcare: A Surveycited by 182
  3. 2021Federated Learning: Opportunities and Challengescited by 83
  4. 2021FedProc: Prototypical Contrastive Federated Learning on Non-IID datacited by 80
  5. 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
  6. 2021Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edgecited by 56
  7. 2021FLOP: Federated Learning on Medical Datasets using Partial Networkscited by 42
  8. 2021Optimality and Stability in Federated Learning: A Game-theoretic Approachcited by 40
  9. 2022RSCFed: Random Sampling Consensus Federated Semi-supervised Learningcited by 32
  10. 2022Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Surveycited by 31
  11. 2022FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Modelcited by 25
  12. 2021Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Diseasecited by 19
38 more
  1. 2024From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcarecited by 16
  2. 2021A Systematic Survey of Blockchained Federated Learningcited by 15
  3. 2022Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imagingcited by 14
  4. 2022Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooringcited by 14
  5. 2024Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challengescited by 14
  6. 2021Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Datacited by 13
  7. 2022Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learningcited by 13
  8. 2022Federated learning: Applications, challenges and future directionscited by 11
  9. 2024AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Modelscited by 11
  10. 2023Blockchain-based Federated Learning with Secure Aggregation in Trusted Execution Environment for Internet-of-Thingscited by 10
  11. 2022Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicinecited by 8
  12. 2023Smart Policy Control for Securing Federated Learning Management Systemcited by 8
  13. 2024Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learningcited by 8
  14. 2023Advancements in Federated Learning: Models, Methods, and Privacycited by 7
  15. 2024Privacy-Preserving in Blockchain-based Federated Learning Systemscited by 7
  16. 2021Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directionscited by 6
  17. 2021Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensingcited by 6
  18. 2021Blockchain-based Trustworthy Federated Learning Architecturecited by 5
  19. 2023Blockchained Federated Learning for Internet of Things: A Comprehensive Surveycited by 5
  20. 2024Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutionscited by 5
  21. 2021Implicit Model Specialization through DAG-based Decentralized Federated Learningcited by 4
  22. 2022A Comprehensive Survey on Federated Learning: Concept and Applicationscited by 4
  23. 2023A Comparative Study of Federated Learning Models for COVID-19 Detectioncited by 3
  24. 2022A Two-Stage Federated Transfer Learning Framework in Medical Images Classification on Limited Data: A COVID-19 Case Studycited by 2
  25. 2024Proof-of-Collaborative-Learning: A Multi-winner Federated Learning Consensus Algorithmcited by 2
  26. 2025Federated Learning for Large Models in Medical Imaging: A Comprehensive Reviewcited by 2
  27. 2021Systematic investigation into generalization of COVID-19 CT deep learning models with Gabor ensemble for lung involvement scoringcited by 1
  28. 2021A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imagingcited by 1
  29. 2021DPCOVID: Privacy-Preserving Federated Covid-19 Detectioncited by 1
  30. 2022ST-FL: Style Transfer Preprocessing in Federated Learning for COVID-19 Segmentationcited by 1
  31. 2024Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Reviewcited by 1
  32. 2025Can Large Language Models Challenge CNNs in Medical Image Analysis?cited by 1
  33. 2021Collective Intelligence: Decentralized Learning for Android Malware Detection in IoT with Blockchaincited by 0
  34. 2021Blockchain based Privacy-Preserved Federated Learning for Medical Images: A Case Study of COVID-19 CT Scanscited by 0
  35. 2021COVID-19 Detection in Computed Tomography Images with 2D and 3D Approachescited by 0
  36. 2022Securing AI-based Healthcare Systems using Blockchain Technology: A State-of-the-Art Systematic Literature Review and Future Research Directionscited by 0
  37. 2022A review of Deep learning Techniques for COVID-19 identification on Chest CT imagescited by 0
  38. 2023Deep Anatomical Federated Network (Dafne): An open client-server framework for the continuous, collaborative improvement of deep learning-based medical image segmentationcited by 0

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