mlsanityData to 7 Oct 2026

Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge

arXiv 2101.07511 ↗First posted 19 Jan 2021cs.LG cs.DCcited by 56

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Abstract (arXiv)

Despite significant improvements over the last few years, cloud-based healthcare applications continue to suffer from poor adoption due to their limitations in meeting stringent security, privacy, and quality of service requirements (such as low latency). The edge computing trend, along with techniques for distributed machine learning such as federated learning, have gained popularity as a viable solution in such settings. In this paper, we leverage the capabilities of edge computing in medicine by analyzing and evaluating the potential of intelligent processing of clinical visual data at the edge allowing the remote healthcare centers, lacking advanced diagnostic facilities, to benefit from the multi-modal data securely. To this aim, we utilize the emerging concept of clustered federated learning (CFL) for an automatic diagnosis of COVID-19. Such an automated system can help reduce the burden on healthcare systems across the world that has been under a lot of stress since the COVID-19 pandemic emerged in late 2019. We evaluate the performance of the proposed framework under different experimental setups on two benchmark datasets. Promising results are obtained on both datasets resulting in comparable results against the central baseline where the specialized models (i.e., each on a specific type of COVID-19 imagery) are trained with central data, and improvements of 16% and 11% in overall F1-Scores have been achieved over the multi-modal model trained in the conventional Federated Learning setup on X-ray and Ultrasound datasets, respectively. We also discuss in detail the associated challenges, technologies, tools, and techniques available for deploying ML at the edge in such privacy and delay-sensitive applications.

Key facts. Cited by 56 papers in the map (data through 7 Oct 2026). Builds on 37 of its 77 references that are in the map. First posted on arXiv 19 Jan 2021.

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37 of 77 references.

References · 37

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25 more
  1. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
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Citing papers per year: 2021: 8; 2022: 12; 2023: 8; 2024: 10; 2025: 14; 2026: 4.

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  1. 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
  2. 2021FedSoft: Soft Clustered Federated Learning with Proximal Local Updatingcited by 46
  3. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 34
  4. 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
  5. 2021Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Diseasecited by 19
  6. 2023Multimodal Federated Learning in Healthcare: a Reviewcited by 13
  7. 2025Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Surveycited by 13
  8. 2022Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoTcited by 12
  9. 2024Green Federated Learning: A new era of Green Aware AIcited by 12
  10. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  11. 2021FedSLD: Federated Learning with Shared Label Distribution for Medical Image Classificationcited by 10
  12. 2022Collaborative Training of Medical Artificial Intelligence Models with non-uniform Labelscited by 10
38 more
  1. 2023Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sourcescited by 10
  2. 2024Co-clustering for Federated Recommender Systemcited by 9
  3. 2024Examining Modality Incongruity in Multimodal Federated Learning for Medical Vision and Language-based Disease Detectioncited by 8
  4. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  5. 2025Multimodal Federated Learning With Missing Modalities through Feature Imputation Networkcited by 8
  6. 2022Federated Remote Physiological Measurement with Imperfect Datacited by 7
  7. 2022ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Schemecited by 7
  8. 2021Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directionscited by 6
  9. 2021Cross-Domain Federated Learning in Medical Imagingcited by 6
  10. 2024Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced Modalitiescited by 6
  11. 2025Boosting multi-demographic federated learning for chest radiograph analysis using general-purpose self-supervised representationscited by 6
  12. 2023Mitigating Group Bias in Federated Learning for Heterogeneous Devicescited by 5
  13. 2024CAR-MFL: Cross-Modal Augmentation by Retrieval for Multimodal Federated Learning with Missing Modalitiescited by 5
  14. 2025UniTrans: A Unified Vertical Federated Knowledge Transfer Framework for Enhancing Cross-Hospital Collaborationcited by 5
  15. 2022A Comprehensive Survey on Federated Learning: Concept and Applicationscited by 4
  16. 2022FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devicescited by 4
  17. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  18. 2021The Duo of Artificial Intelligence and Big Data for Industry 4.0: Review of Applications, Techniques, Challenges, and Future Research Directionscited by 3
  19. 2022Secure and Trustworthy Artificial Intelligence-Extended Reality (AI-XR) for Metaversescited by 3
  20. 2023PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clientscited by 3
  21. 2023Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Surveycited by 3
  22. 2021Sentiment Analysis of Users' Reviews on COVID-19 Contact Tracing Apps with a Benchmark Datasetcited by 2
  23. 2022A Two-Stage Federated Transfer Learning Framework in Medical Images Classification on Limited Data: A COVID-19 Case Studycited by 2
  24. 2022A Secure Healthcare 5.0 System Based on Blockchain Technology Entangled with Federated Learning Techniquecited by 2
  25. 2023Cross-device Federated Learning for Mobile Health Diagnostics: A First Study on COVID-19 Detectioncited by 2
  26. 2024Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systemscited by 2
  27. 2025A survey on Clustered Federated Learning: Taxonomy, Analysis and Applicationscited by 2
  28. 2025Federated Learning for Large Models in Medical Imaging: A Comprehensive Reviewcited by 2
  29. 2022Federated Learning for Chronic Obstructive Pulmonary Disease Classification with Partial Personalized Attention Mechanismcited by 1
  30. 2025A Multi-Modal Federated Learning Framework for Remote Sensing Image Classificationcited by 1
  31. 2025Federated learning, ethics, and the double black box problem in medical AIcited by 1
  32. 2025Minimizing Layerwise Activation Norm Improves Generalization in Federated Learningcited by 1
  33. 2021Multimodal Detection of COVID-19 Symptoms using Deep Learning & Probability-based Weighting of Modescited by 0
  34. 2024Federated Diabetes Prediction in Canadian Adults Using Real-world Cross-Province Primary Care Datacited by 0
  35. 2024FedRepOpt: Gradient Re-parametrized Optimizers in Federated Learningcited by 0
  36. 2025Asynchronous Personalized Federated Learning through Global Memorizationcited by 0
  37. 2025CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detectioncited by 0
  38. 2025Personalized Federated Learning under Model Dissimilarity Constraintscited by 0

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