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

Security and Privacy of mHealth and uHealth (116) Machine Learning (1370) Wearable Devices and Sensors (811) Ethics, Privacy, and Legal Issues (456) mHealth for Data Collection and Research (907)

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 (27) Tweetations (3) Metrics

Journals

  1. Liu Y, Bi D. Quantitative risk analysis of treatment plans for patients with tumor by mining historical similar patients from electronic health records using federated learning. Risk Analysis 2023;43(12):2422 View
  2. Han B, Jhaveri R, Wang H, Qiao D, Du J. Application of Robust Zero-Watermarking Scheme Based on Federated Learning for Securing the Healthcare Data. IEEE Journal of Biomedical and Health Informatics 2023;27(2):804 View
  3. Joshi M, Pal A, Sankarasubbu M. Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges. ACM Transactions on Computing for Healthcare 2022;3(4):1 View
  4. Antunes R, André da Costa C, Küderle A, Yari I, Eskofier B. Federated Learning for Healthcare: Systematic Review and Architecture Proposal. ACM Transactions on Intelligent Systems and Technology 2022;13(4):1 View
  5. Fauzi M, Yang B, Blobel B. Comparative Analysis between Individual, Centralized, and Federated Learning for Smartwatch Based Stress Detection. Journal of Personalized Medicine 2022;12(10):1584 View
  6. Prayitno , Shyu C, Putra K, Chen H, Tsai Y, Hossain K, Jiang W, Shae Z. A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications. Applied Sciences 2021;11(23):11191 View
  7. Wang T, Du Y, Gong Y, Choo K, Guo Y. Applications of Federated Learning in Mobile Health: Scoping Review. Journal of Medical Internet Research 2023;25:e43006 View
  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
  13. Shiranthika C, Saeedi P, Bajić I. Decentralized Learning in Healthcare: A Review of Emerging Techniques. IEEE Access 2023;11:54188 View
  14. Rani S, Kataria A, Kumar S, Tiwari P. Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review. Knowledge-Based Systems 2023;274:110658 View
  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

Books/Policy Documents

  1. Boumpa E, Tsoukas V, Gkogkidis A, Spathoulas G, Kakarountas A. Wireless Mobile Communication and Healthcare. View
  2. Singh A, Kumar A, Choi B. Intelligent Human Computer Interaction. View
  3. Young M. Intracranial EEG. View
  4. Barry G, Konyar E, Harvill B, Johnstone C. Multimodal and Tensor Data Analytics for Industrial Systems Improvement. View