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
.

Journals
- 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 View
- 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
- 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
- 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
- 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
- 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
- Wang T, Du Y, Gong Y, Choo K, Guo Y. Applications of Federated Learning in Mobile Health: Scoping Review (Preprint). Journal of Medical Internet Research 2022 View
- 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
Books/Policy Documents
- Boumpa E, Tsoukas V, Gkogkidis A, Spathoulas G, Kakarountas A. Wireless Mobile Communication and Healthcare. View