This paper is in the following e-collection/theme issue:

Security and Privacy of mHealth and uHealth (126) Machine Learning (1501) Wearable Devices and Sensors (903) Ethics, Privacy, and Legal Issues (494) mHealth for Data Collection and Research (981)

Published on in Vol 9, No 3 (2021): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23728, first published .
Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data

Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data

Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data

Authors of this article:

Jessica Chia Liu1 Author Orcid Image ;   Jack Goetz1 Author Orcid Image ;   Srijan Sen2, 3 Author Orcid Image ;   Ambuj Tewari1 Author Orcid Image
Article Authors Cited by (33) Tweetations (3) Metrics

Journals

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  8. Zhang L, Vashisht H, Totev A, Trinh N, Ward T. A comparison of distributed machine learning methods for the support of “many labs” collaborations in computational modeling of decision making. Frontiers in Psychology 2022;13 View
  9. Tewari A. mHealth Systems Need a Privacy-by-Design Approach: Commentary on “Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review”. Journal of Medical Internet Research 2023;25:e46700 View
  10. Allareddy V, Rampa S, Venugopalan S, Elnagar M, Lee M, Oubaidin M, Yadav S. Blockchain technology and federated machine learning for collaborative initiatives in orthodontics and craniofacial health. Orthodontics & Craniofacial Research 2023;26(S1):118 View
  11. Shen A, Francisco L, Sen S, Tewari A. Exploring the Relationship Between Privacy and Utility in Mobile Health: Algorithm Development and Validation via Simulations of Federated Learning, Differential Privacy, and External Attacks. Journal of Medical Internet Research 2023;25:e43664 View
  12. Yang Y, Lu Y, Mei G. A federated learning based approach for predicting landslide displacement considering data security. Future Generation Computer Systems 2023;149:184 View
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  15. Wang R, Lai J, Li X, He D, Khan M. RPIFL: Reliable and Privacy-Preserving Federated Learning for the Internet of Things. Journal of Network and Computer Applications 2024;221:103768 View
  16. Zeng Y, Teng S, Xiang T, Zhang J, Mu Y, Ren Y, Wan J. A Client Selection Method Based on Loss Function Optimization for Federated Learning. Computer Modeling in Engineering & Sciences 2023;137(1):1047 View
  17. Ghadi Y, Mazhar T, Shah S, Haq I, Ahmad W, Ouahada K, Hamam H. Integration of federated learning with IoT for smart cities applications, challenges, and solutions. PeerJ Computer Science 2023;9:e1657 View
  18. Praba R, Suganthi L. HARNet: automatic recognition of human activity from mobile health data using CNN and transfer learning of LSTM with SVM. Automatika 2024;65(1):167 View
  19. Li J, Washington P. A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study. JMIR AI 2024;3:e52171 View
  20. Alazeb A, Batool M, Mudawi N, Alshehri M, Almakdi S, Almujally N, Algarni A. Effective Gait Abnormality Detection in Parkinson’s Patients for Multi-Sensors Surveillance System. IEEE Access 2024;12:48686 View
  21. De Ridder D, Siddiqi M, Dauwels J, Serdijn W, Strydis C. NeuroDots: From Single-Target to Brain-Network Modulation: Why and What Is Needed?. Neuromodulation: Technology at the Neural Interface 2024;27(4):711 View
  22. Ferrara E. Large Language Models for Wearable Sensor-Based Human Activity Recognition, Health Monitoring, and Behavioral Modeling: A Survey of Early Trends, Datasets, and Challenges. Sensors 2024;24(15):5045 View
  23. Qiu W, Feng Y, Li Y, Chang Y, Qian K, Hu B, Yamamoto Y, Schuller B. Fed-MStacking: Heterogeneous Federated Learning With Stacking Misaligned Labels for Abnormal Heart Sound Detection. IEEE Journal of Biomedical and Health Informatics 2024;28(9):5055 View
  24. Qiu W, Quan C, Yu Y, Kara E, Qian K, Hu B, Schuller B, Yamamoto Y. Federated Abnormal Heart Sound Detection with Weak to No Labels. Cyborg and Bionic Systems 2024;5 View
  25. Flores-Martin D, Laso S, Berrocal J, Murillo J. Towards digital health: Integrating federated learning and crowdsensing through the Contigo app. SoftwareX 2024;28:101885 View
  26. Qiu W, Quan C, Zhu L, Yu Y, Wang Z, Ma Y, Sun M, Chang Y, Qian K, Hu B, Yamamoto Y, Schuller B. Heart Sound Abnormality Detection From Multi-Institutional Collaboration: Introducing a Federated Learning Framework. IEEE Transactions on Biomedical Engineering 2024;71(10):2802 View
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Books/Policy Documents

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