mlsanityData to 7 Oct 2026

Federated Learning for Smart Healthcare: A Survey

arXiv 2111.08834 ↗First posted 16 Nov 2021cs.LG eess.SPcited by 182

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Abstract (arXiv)

Recent advances in communication technologies and Internet-of-Medical-Things have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may be infeasible in realistic healthcare scenarios due to the high scalability of modern healthcare networks and growing data privacy concerns. Federated Learning (FL), as an emerging distributed collaborative AI paradigm, is particularly attractive for smart healthcare, by coordinating multiple clients (e.g., hospitals) to perform AI training without sharing raw data. Accordingly, we provide a comprehensive survey on the use of FL in smart healthcare. First, we present the recent advances in FL, the motivations, and the requirements of using FL in smart healthcare. The recent FL designs for smart healthcare are then discussed, ranging from resource-aware FL, secure and privacy-aware FL to incentive FL and personalized FL. Subsequently, we provide a state-of-the-art review on the emerging applications of FL in key healthcare domains, including health data management, remote health monitoring, medical imaging, and COVID-19 detection. Several recent FL-based smart healthcare projects are analyzed, and the key lessons learned from the survey are also highlighted. Finally, we discuss interesting research challenges and possible directions for future FL research in smart healthcare.

Key facts. Cited by 182 papers in the map (data through 7 Oct 2026). Builds on 73 of its 121 references that are in the map. First posted on arXiv 16 Nov 2021.

Builds on

73 of 121 references.

References · 73

  1. 2018Federated Learning with Non-IID Datacited by 1,099
  2. 2019Deep Leakage from Gradientscited by 1,004
  3. 2019Towards Federated Learning at Scale: System Designcited by 826
  4. 2020The Future of Digital Health with Federated Learningcited by 625
  5. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  6. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  7. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  8. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 479
  9. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  10. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  11. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
  12. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
38 more
  1. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 307
  2. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 266
  3. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
  4. 2018GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classificationcited by 242
  5. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
  6. 2016Practical Secure Aggregation for Federated Learning on User-Held Datacited by 178
  7. 2018Machine Learning with Membership Privacy using Adversarial Regularizationcited by 177
  8. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  9. 2017Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysiscited by 145
  10. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 138
  11. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  12. 2020Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Resultscited by 122
  13. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 108
  14. 2019Reliable Federated Learning for Mobile Networkscited by 92
  15. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  16. 2020Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learningcited by 78
  17. 2021Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learningcited by 78
  18. 2019Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanismcited by 75
  19. 2021Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challengescited by 75
  20. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  21. 2019Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Recordscited by 70
  22. 2019Decentralized Federated Learning: A Segmented Gossip Approachcited by 63
  23. 2020Federated Learning for Breast Density Classification: A Real-World Implementationcited by 62
  24. 2020FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoringcited by 61
  25. 2020Multi-Armed Bandit Based Client Scheduling for Federated Learningcited by 56
  26. 2020Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imagingcited by 56
  27. 2019On Safeguarding Privacy and Security in the Framework of Federated Learningcited by 55
  28. 2021Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocationcited by 55
  29. 2020Federated Learning for Computational Pathology on Gigapixel Whole Slide Imagescited by 54
  30. 2019Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinatorcited by 51
  31. 2018Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Datacited by 46
  32. 2019Motivating Workers in Federated Learning: A Stackelberg Game Perspectivecited by 45
  33. 2019Differential Privacy-enabled Federated Learning for Sensitive Health Datacited by 44
  34. 2017Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Recordscited by 36
  35. 2020Dynamic Fusion based Federated Learning for COVID-19 Detectioncited by 36
  36. 2021CaPC Learning: Confidential and Private Collaborative Learningcited by 33
  37. 2020Experiments of Federated Learning for COVID-19 Chest X-ray Imagescited by 31
  38. 2021Fusion of Federated Learning and Industrial Internet of Things: A Surveycited by 28

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 11; 2023: 43; 2024: 61; 2025: 50; 2026: 17.

182 papers.

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  1. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
  2. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  3. 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
  4. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 34
  5. 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
  6. 2024Towards Efficient Replay in Federated Incremental Learningcited by 33
  7. 2024Advances and Open Challenges in Federated Foundation Modelscited by 33
  8. 2022Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Surveycited by 31
  9. 2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Surveycited by 23
  10. 2024Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learningcited by 23
  11. 2022Faster federated optimization under second-order similaritycited by 18
  12. 2023FedICT: Federated Multi-task Distillation for Multi-access Edge Computingcited by 17
38 more
  1. 2023Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methodscited by 17
  2. 2023FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learningcited by 17
  3. 2024FedMM: Federated Multi-Modal Learning with Modality Heterogeneity in Computational Pathologycited by 17
  4. 2023A Survey for Federated Learning Evaluations: Goals and Measurescited by 16
  5. 2024A Survey on Large Language Models from General Purpose to Medical Applications: Datasets, Methodologies, and Evaluationscited by 16
  6. 2023Hybrid quantum image classification and federated learning for hepatic steatosis diagnosiscited by 15
  7. 2023AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devicescited by 15
  8. 2024Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligencecited by 15
  9. 2023Remote patient monitoring using artificial intelligence: Current state, applications, and challengescited by 14
  10. 2024Federated Distillation: A Surveycited by 14
  11. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  12. 2023FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labelscited by 13
  13. 2024Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregationcited by 13
  14. 2025Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspectioncited by 13
  15. 2025Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Surveycited by 13
  16. 2023Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learningcited by 12
  17. 2023FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learningcited by 12
  18. 2024TrimCaching: Parameter-sharing AI Model Caching in Wireless Edge Networkscited by 12
  19. 2024Green Federated Learning: A new era of Green Aware AIcited by 12
  20. 2023Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysiscited by 11
  21. 2023FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategycited by 10
  22. 2024Poisoning Attacks on Federated Learning-based Wireless Traffic Predictioncited by 10
  23. 2024Adaptive Gradient Clipping for Robust Federated Learningcited by 10
  24. 2022Study of the performance and scalability of federated learning for medical imaging with intermittent clientscited by 9
  25. 2023A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspectivecited by 9
  26. 2022Federated Learning Using Variance Reduced Stochastic Gradient for Probabilistically Activated Agentscited by 8
  27. 2023Vertical Federated Learning: Taxonomies, Threats, and Prospectscited by 8
  28. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  29. 2024An Explainable AI Framework for Artificial Intelligence of Medical Thingscited by 8
  30. 2024FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injectioncited by 8
  31. 2024Federated Learning with Quantum Computing and Fully Homomorphic Encryption: A Novel Computing Paradigm Shift in Privacy-Preserving MLcited by 8
  32. 2023Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharingcited by 7
  33. 2023Privacy Risks Analysis and Mitigation in Federated Learning for Medical Imagescited by 7
  34. 2024COALA: A Practical and Vision-Centric Federated Learning Platformcited by 7
  35. 2024Federated Learning with Label-Masking Distillationcited by 7
  36. 2025Mitigating Unintended Memorization with LoRA in Federated Learning for LLMscited by 6
  37. 2025Federated Learning for Cyber Physical Systems: A Comprehensive Surveycited by 6
  38. 2024Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutionscited by 5

50 most cited of 182 All 182 (JSON)