mlsanityData to 14 Oct 2026

Differential Privacy-enabled Federated Learning for Sensitive Health Data

arXiv 1910.02578 ↗First posted 7 Oct 2019cs.LG cs.CRcited by 44

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Abstract (arXiv)

Leveraging real-world health data for machine learning tasks requires addressing many practical challenges, such as distributed data silos, privacy concerns with creating a centralized database from person-specific sensitive data, resource constraints for transferring and integrating data from multiple sites, and risk of a single point of failure. In this paper, we introduce a federated learning framework that can learn a global model from distributed health data held locally at different sites. The framework offers two levels of privacy protection. First, it does not move or share raw data across sites or with a centralized server during the model training process. Second, it uses a differential privacy mechanism to further protect the model from potential privacy attacks. We perform a comprehensive evaluation of our approach on two healthcare applications, using real-world electronic health data of 1 million patients. We demonstrate the feasibility and effectiveness of the federated learning framework in offering an elevated level of privacy and maintaining utility of the global model.

Key facts. Cited by 44 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Oct 2019.

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Citing papers per year: 2021: 11; 2022: 8; 2023: 8; 2024: 13; 2025: 4.

44 papers.

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  1. 2021Federated Learning for Smart Healthcare: A Surveycited by 184
  2. 2021Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learningcited by 111
  3. 2021Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacycited by 69
  4. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 35
  5. 2022Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Surveycited by 31
  6. 2023On the Privacy-Robustness-Utility Trilemma in Distributed Learningcited by 24
  7. 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24
  8. 2021Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregationcited by 22
  9. 2023Federated Learning for Computer Visioncited by 22
  10. 2021Algorithm Fairness in AI for Medicine and Healthcarecited by 21
  11. 2022APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learningcited by 20
  12. 2021Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligencecited by 18
32 more
  1. 2021A Systematic Survey of Blockchained Federated Learningcited by 15
  2. 2022Adap DP-FL: Differentially Private Federated Learning with Adaptive Noisecited by 13
  3. 2021Failure Prediction in Production Line Based on Federated Learning: An Empirical Studycited by 11
  4. 2023Client-specific Property Inference against Secure Aggregation in Federated Learningcited by 9
  5. 2024Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimationcited by 9
  6. 2021Personalized Federated Deep Learning for Pain Estimation From Face Imagescited by 8
  7. 2022Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray Datacited by 8
  8. 2025Towards Trustworthy Federated Learning with Untrusted Participantscited by 8
  9. 2025Agentic AI framework for End-to-End Medical Data Inferencecited by 8
  10. 2021Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutionscited by 7
  11. 2023FederatedTrust: A Solution for Trustworthy Federated Learningcited by 6
  12. 2024Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Biomedical Data Analysiscited by 6
  13. 2025Federated Learning for Cyber Physical Systems: A Comprehensive Surveycited by 6
  14. 2025FedMentor: Domain-Aware Differential Privacy for Heterogeneous Federated LLMs in Mental Healthcited by 5
  15. 2023Federated and distributed learning applications for electronic health records and structured medical data: A scoping reviewcited by 4
  16. 2024Open-Source AI-based SE Tools: Opportunities and Challenges of Collaborative Software Learningcited by 4
  17. 2022"You Can't Fix What You Can't Measure": Privately Measuring Demographic Performance Disparities in Federated Learningcited by 3
  18. 2024Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectivescited by 3
  19. 2022Local Model Reconstruction Attacks in Federated Learning and their Usescited by 2
  20. 2024Attention on Personalized Clinical Decision Support System: Federated Learning Approachcited by 2
  21. 2024On-device Federated Learning in Smartphones for Detecting Depression from Reddit Postscited by 2
  22. 2021Optimizing the Numbers of Queries and Replies in Federated Learning with Differential Privacycited by 1
  23. 2023Exploratory Analysis of Federated Learning Methods with Differential Privacy on MIMIC-IIIcited by 1
  24. 2024Secure Federated Learning Approaches to Diagnosing COVID-19cited by 1
  25. 2024Federated Learning in Genetics: Extended Analysis of Accuracy, Performance and Privacy Trade-offscited by 1
  26. 2024Privacy-Preserving Heterogeneous Federated Learning for Sensitive Healthcare Datacited by 1
  27. 2024SSSD-ECG-nle: New Label Embeddings with Structured State-Space Models for ECG generationcited by 1
  28. 2024Attribute Inference Attacks for Federated Regression Taskscited by 1
  29. 2022Federated learning for violence incident prediction in a simulated cross-institutional psychiatric settingcited by 0
  30. 2023Using Decentralized Aggregation for Federated Learning with Differential Privacycited by 0
  31. 2024A Federated Parameter Aggregation Method for Node Classification Tasks with Different Graph Network Structurescited by 0
  32. 2024Upcycling Noise for Federated Unlearningcited by 0