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FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare

arXiv 1907.09173 ↗First posted 22 Jul 2019cs.LG cs.AI cs.NEcited by 182

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Abstract (arXiv)

With the rapid development of computing technology, wearable devices such as smart phones and wristbands make it easy to get access to people's health information including activities, sleep, sports, etc. Smart healthcare achieves great success by training machine learning models on a large quantity of user data. However, there are two critical challenges. Firstly, user data often exists in the form of isolated islands, making it difficult to perform aggregation without compromising privacy security. Secondly, the models trained on the cloud fail on personalization. In this paper, we propose FedHealth, the first federated transfer learning framework for wearable healthcare to tackle these challenges. FedHealth performs data aggregation through federated learning, and then builds personalized models by transfer learning. It is able to achieve accurate and personalized healthcare without compromising privacy and security. Experiments demonstrate that FedHealth produces higher accuracy (5.3% improvement) for wearable activity recognition when compared to traditional methods. FedHealth is general and extensible and has the potential to be used in many healthcare applications.

Key facts. Cited by 182 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Jul 2019.

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Citing papers per year: 2021: 40; 2022: 37; 2023: 40; 2024: 36; 2025: 20; 2026: 9.

182 papers.

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  1. 2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 766
  2. 2021Towards Personalized Federated Learningcited by 464
  3. 2021Federated Learning for Smart Healthcare: A Surveycited by 184
  4. 2021FedBABU: Towards Enhanced Representation for Federated Image Classificationcited by 182
  5. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 161
  6. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  7. 2023Domain Generalization for Medical Image Analysis: A Reviewcited by 48
  8. 2021Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signalscited by 41
  9. 2023A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Taskscited by 41
  10. 2021Communication Efficiency in Federated Learning: Achievements and Challengescited by 40
  11. 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
  12. 2023Eliminating Domain Bias for Federated Learning in Representation Spacecited by 38
38 more
  1. 2022MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcarecited by 37
  2. 2021Non-IID data and Continual Learning processes in Federated Learning: A long road aheadcited by 36
  3. 2021Emerging Trends in Federated Learning: From Model Fusion to Federated X Learningcited by 35
  4. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 35
  5. 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
  6. 2023Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directionscited by 33
  7. 2021Multimodal Federated Learning on IoT Datacited by 32
  8. 2021A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparisoncited by 31
  9. 2022Achieving Personalized Federated Learning with Sparse Local Modelscited by 31
  10. 2022Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Surveycited by 31
  11. 2022FedFM: Anchor-based Feature Matching for Data Heterogeneity in Federated Learningcited by 27
  12. 2023A Survey on Decentralized Federated Learningcited by 27
  13. 2021Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AIcited by 26
  14. 2021FedAdapt: Adaptive Offloading for IoT Devices in Federated Learningcited by 26
  15. 2021Personalized Federated Learning with Adaptive Batchnorm for Healthcarecited by 26
  16. 2023Federated Domain Generalization: A Surveycited by 25
  17. 2022FedMix: Mixed Supervised Federated Learning for Medical Image Segmentationcited by 23
  18. 2022AI Security for Geoscience and Remote Sensing: Challenges and Future Trendscited by 23
  19. 2021EasyFL: A Low-code Federated Learning Platform For Dummiescited by 20
  20. 2021Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Settingcited by 19
  21. 2023Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networkscited by 19
  22. 2021Transfer Learning for Future Wireless Networks: A Comprehensive Surveycited by 18
  23. 2021Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryptioncited by 18
  24. 2023Visual Prompt Based Personalized Federated Learningcited by 16
  25. 2024Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detectioncited by 16
  26. 2024From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcarecited by 16
  27. 2021A Systematic Survey of Blockchained Federated Learningcited by 15
  28. 2021Decentralized Unsupervised Learning of Visual Representationscited by 15
  29. 2024Federated Distillation: A Surveycited by 15
  30. 2023Remote patient monitoring using artificial intelligence: Current state, applications, and challengescited by 14
  31. 2024Transfer Learning in Human Activity Recognition: A Surveycited by 14
  32. 2024MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysiscited by 14
  33. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  34. 2021Federated Multi-View Learning for Private Medical Data Integration and Analysiscited by 12
  35. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
  36. 2022HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networkscited by 12
  37. 2023Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learningcited by 12
  38. 2021Self-supervised Cross-silo Federated Neural Architecture Searchcited by 11

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