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

Digital Health Reviews (1533) Mobile Health (mhealth) (3164) Decision Support for Health Professionals (1345) Clinical Information and Decision Making (1664)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43006, first published .
Applications of Federated Learning in Mobile Health: Scoping Review

Applications of Federated Learning in Mobile Health: Scoping Review

Applications of Federated Learning in Mobile Health: Scoping Review

Authors of this article:

Tongnian Wang1 Author Orcid Image ;   Yan Du2 Author Orcid Image ;   Yanmin Gong3 Author Orcid Image ;   Kim-Kwang Raymond Choo1 Author Orcid Image ;   Yuanxiong Guo1 Author Orcid Image
Article Authors Cited by (18) Tweetations (4) Metrics

Journals

  1. Liang X, Zhao J, Chen Y, Bandara E, Shetty S. Architectural Design of a Blockchain-Enabled, Federated Learning Platform for Algorithmic Fairness in Predictive Health Care: Design Science Study. Journal of Medical Internet Research 2023;25:e46547 View
  2. Fan K, Xu C, Cao X, Jiao K, Mo W. Tri-branch feature pyramid network based on federated particle swarm optimization for polyp segmentation. Mathematical Biosciences and Engineering 2024;21(1):1610 View
  3. Wang T, Zhang K, Cai J, Gong Y, Choo K, Guo Y. Analyzing the Impact of Personalization on Fairness in Federated Learning for Healthcare. Journal of Healthcare Informatics Research 2024;8(2):181 View
  4. Al-masni M, Marzban E, Al-Shamiri A, Al-antari M, Alabdulhafith M, Mahmoud N, Abdel Samee N, Kadah Y. Gait Impairment Analysis Using Silhouette Sinogram Signals and Assisted Knowledge Learning. Bioengineering 2024;11(5):477 View
  5. Freitas J, Bischof O. Computational modeling of aging-related gene networks: a review. Frontiers in Applied Mathematics and Statistics 2024;10 View
  6. Belal Y, Ben Mokhtar S, Haddadi H, Wang J, Mashhadi A. Survey of Federated Learning Models for Spatial-Temporal Mobility Applications. ACM Transactions on Spatial Algorithms and Systems 2024;10(3):1 View
  7. Majeed A, Hwang S. A Multifaceted Survey on Federated Learning: Fundamentals, Paradigm Shifts, Practical Issues, Recent Developments, Partnerships, Trade-Offs, Trustworthiness, and Ways Forward. IEEE Access 2024;12:84643 View
  8. Manzoor H, Shabbir A, Chen A, Flynn D, Zoha A. A Survey of Security Strategies in Federated Learning: Defending Models, Data, and Privacy. Future Internet 2024;16(10):374 View
  9. Zhou Y, Wang J, Kong X, Wu S, Xie X, Qi H. Exploring Amplified Heterogeneity Arising From Heavy-Tailed Distributions in Federated Learning. IEEE Transactions on Mobile Computing 2024;23(12):11519 View
  10. Huang G, Wu Q, Li J, Chen X. IMFL-AIGC: Incentive Mechanism Design for Federated Learning Empowered by Artificial Intelligence Generated Content. IEEE Transactions on Mobile Computing 2024;23(12):12603 View
  11. Vajrobol V, Singh S, Saxena G, Pundir A, Gaurav A, Bansal S, Attar R, Rahman M, Gupta B. A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare. Computer Modeling in Engineering & Sciences 2025;142(1):49 View
  12. Rishu ., Kukreja V, Hariharan S, Dogra A, Goyal B. Information Modeling Technique to Decipher Research Trends of Federated Learning in Healthcare. The Open Neuroimaging Journal 2025;18(1) View
  13. Kapsecker M, Jonas S, Tsaneva-Atanasova K. Cross-device federated unsupervised learning for the detection of anomalies in single-lead electrocardiogram signals. PLOS Digital Health 2025;4(4):e0000793 View
  14. Yang M, Ngai E, Hu X, Hu B, Liu J, Gelenbe E, Leung V. Digital Phenotyping and Feature Extraction on Smartphone Data for Depression Detection. Proceedings of the IEEE 2024;112(12):1773 View

Books/Policy Documents

  1. Chengxi H, Rajawat A, Goyal S, Solanki R. Intelligent Computing and Optimization. View

Conference Proceedings

  1. Negi V, Chinara S. 2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). Study of MobileNets Model in Federated Learning View
  2. Sharma A, Tripathi T, Majumdar A. 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). Enhancing Edge-based Cardiovascular Diagnosis through Federated Learning and IoT View
  3. Sharma P, Kashniyal J, ESham E. 2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE). An Insight into Federated Learning: A Collaborative Approach for Machine Learning View