mlsanityData to 7 Oct 2026

Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey

arXiv 2203.09702 ↗First posted 18 Mar 2022eess.SY cs.CR cs.LGcited by 31

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Abstract (arXiv)

Recent advances in electronic devices and communication infrastructure have revolutionized the traditional healthcare system into a smart healthcare system by using IoMT devices. However, due to the centralized training approach of artificial intelligence (AI), the use of mobile and wearable IoMT devices raises privacy concerns with respect to the information that has been communicated between hospitals and end users. The information conveyed by the IoMT devices is highly confidential and can be exposed to adversaries. In this regard, federated learning (FL), a distributive AI paradigm has opened up new opportunities for privacy-preservation in IoMT without accessing the confidential data of the participants. Further, FL provides privacy to end users as only gradients are shared during training. For these specific properties of FL, in this paper we present privacy related issues in IoMT. Afterwards, we present the role of FL in IoMT networks for privacy preservation and introduce some advanced FL architectures incorporating deep reinforcement learning (DRL), digital twin, and generative adversarial networks (GANs) for detecting privacy threats. Subsequently, we present some practical opportunities of FL in smart healthcare systems. At the end, we conclude this survey by providing open research challenges for FL that can be used in future smart healthcare systems

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Builds on 40 of its 108 references that are in the map. First posted on arXiv 18 Mar 2022.

Builds on

40 of 108 references.

References · 40

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Deep Leakage from Gradientscited by 1,004
  3. 2020The Future of Digital Health with Federated Learningcited by 625
  4. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  5. 2017Wild Patterns: Ten Years After the Rise of Adversarial Machine Learningcited by 411
  6. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
  7. 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 326
  8. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
  9. 2021Federated Learning for Smart Healthcare: A Surveycited by 182
  10. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  11. 2018Machine Learning with Membership Privacy using Adversarial Regularizationcited by 177
  12. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
28 more
  1. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 138
  2. 2019Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanismcited by 75
  3. 2020FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoringcited by 61
  4. 2020Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imagingcited by 56
  5. 2019Motivating Workers in Federated Learning: A Stackelberg Game Perspectivecited by 45
  6. 2019Differential Privacy-enabled Federated Learning for Sensitive Health Datacited by 44
  7. 2020Dynamic Fusion based Federated Learning for COVID-19 Detectioncited by 36
  8. 2021CaPC Learning: Confidential and Private Collaborative Learningcited by 33
  9. 2020Experiments of Federated Learning for COVID-19 Chest X-ray Imagescited by 31
  10. 2020Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networkscited by 30
  11. 2021Fusion of Federated Learning and Industrial Internet of Things: A Surveycited by 28
  12. 2021Grey-box Adversarial Attack And Defence For Sentiment Classificationcited by 28
  13. 2020Adaptive Federated Learning and Digital Twin for Industrial Internet of Thingscited by 27
  14. 2020A Federated Learning Framework for Healthcare IoT devicescited by 22
  15. 2021Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directionscited by 22
  16. 2019Two-stage Federated Phenotyping and Patient Representation Learningcited by 19
  17. 2019Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networkscited by 16
  18. 2021Hierarchical Federated Learning based Anomaly Detection using Digital Twins for Smart Healthcarecited by 15
  19. 2021Federated Learning for Industrial Internet of Things in Future Industriescited by 14
  20. 2021Efficient Client Contribution Evaluation for Horizontal Federated Learningcited by 12
  21. 2020FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applicationscited by 10
  22. 2020Two Sides of the Same Coin: White-box and Black-box Attacks for Transfer Learningcited by 9
  23. 2020Federated Learning with Heterogeneous Labels and Models for Mobile Activity Monitoringcited by 8
  24. 2020Toward Smart Security Enhancement of Federated Learning Networkscited by 6
  25. 2019Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligencecited by 5
  26. 2021Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Artscited by 5
  27. 2021Blockchain and AI-based Solutions to Combat Coronavirus (COVID-19)-like Epidemics: A Surveycited by 4
  28. 2017Privacy Guidelines for Internet of Things: A Cheat Sheetcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 5; 2024: 9; 2025: 7; 2026: 7.

31 papers.

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  1. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 269
  2. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 34
  3. 2024Generative AI for the Optimization of Next-Generation Wireless Networks: Basics, State-of-the-Art, and Open Challengescited by 14
  4. 2022A Review of Federated Learning in Energy Systemscited by 9
  5. 2025Diffusion Models for Computational Neuroimaging: A Surveycited by 5
  6. 2023Privacy-preserving machine learning for healthcare: open challenges and future perspectivescited by 4
  7. 2023MIA-BAD: An Approach for Enhancing Membership Inference Attack and its Mitigation with Federated Learningcited by 4
  8. 2024Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learningcited by 3
  9. 2025Don't Reach for the Stars: Rethinking Topology for Resilient Federated Learningcited by 3
  10. 2023Towards Real-World Applications of Personalized Anesthesia Using Policy Constraint Q Learning for Propofol Infusion Controlcited by 2
  11. 2023A Survey on Federated Learning for the Healthcare Metaverse: Concepts, Applications, Challenges, and Future Directionscited by 2
  12. 2023Sparse Decentralized Federated Learningcited by 2
19 more
  1. 2024Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentationcited by 2
  2. 2025Federated Learning for Large Models in Medical Imaging: A Comprehensive Reviewcited by 2
  3. 2024Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimizationcited by 1
  4. 2024In-depth Analysis of Privacy Threats in Federated Learning for Medical Datacited by 1
  5. 2024A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoTcited by 1
  6. 2024Generative AI in Medicinecited by 1
  7. 2025SoftAdaClip: A Smooth Clipping Strategy for Fair and Private Model Trainingcited by 1
  8. 2026DSFedMed: Dual-Scale Federated Medical Image Segmentation via Mutual Distillation Between Foundation and Lightweight Modelscited by 1
  9. 2026BiFedKD: Bidirectional Federated Knowledge Distillation Framework for Non-IID and Long-Tailed ECG Monitoringcited by 1
  10. 2024Unsupervised Federated Optimization at the Edge: D2D-Enabled Learning without Labelscited by 0
  11. 2024Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic reviewcited by 0
  12. 2025Principles and Components of Federated Learning Architecturescited by 0
  13. 2025Defending Against Gradient Inversion Attacks for Biomedical Images via Learnable Data Perturbationcited by 0
  14. 2025HiLWS: A Human-in-the-Loop Weak Supervision Framework for Curating Clinical and Home Video Data for Neurological Assessmentcited by 0
  15. 2026Development and Design of FLKit: A Structured Onboarding Toolkit for Federated Learning in Health and Life Sciencescited by 0
  16. 2026BettiSplit: Topology-Guided Privacy-Aware Split Learning Against Feature Inversion and Gradient Leakagecited by 0
  17. 2026Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learningcited by 0
  18. 2026BioSync: Transformer-Based Cross-Modal Fusion for a Multimodal Physiological Digital Biomarkercited by 0
  19. 2026Multi-source conformal prediction: leveraging heterogeneity via localizationcited by 0