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

Formative Evaluation of Digital Health Interventions (2226) Ethics, Privacy, and Legal Issues (458) Machine Learning (1400)

Published on in Vol 4, No 12 (2020): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17265, first published .
Stochastic Channel-Based Federated Learning With Neural Network Pruning for Medical Data Privacy Preservation: Model Development and Experimental Validation

Stochastic Channel-Based Federated Learning With Neural Network Pruning for Medical Data Privacy Preservation: Model Development and Experimental Validation

Stochastic Channel-Based Federated Learning With Neural Network Pruning for Medical Data Privacy Preservation: Model Development and Experimental Validation

Authors of this article:

Rulin Shao1 Author Orcid Image ;   Hongyu He2 Author Orcid Image ;   Ziwei Chen3 Author Orcid Image ;   Hui Liu4 Author Orcid Image ;   Dianbo Liu5 Author Orcid Image
Article Authors Cited by (5) Tweetations (3) Metrics

Journals

  1. Ficek J, Wang W, Chen H, Dagne G, Daley E. Differential privacy in health research: A scoping review. Journal of the American Medical Informatics Association 2021;28(10):2269 View
  2. 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
  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. Oh W, Nadkarni G. Federated Learning in Health care Using Structured Medical Data. Advances in Kidney Disease and Health 2023;30(1):4 View

Books/Policy Documents

  1. Kanhegaonkar P, Prakash S. Data Fusion Techniques and Applications for Smart Healthcare. View