Stochastic Channel-Based Federated Learning With Neural Network Pruning for Medical Data Privacy Preservation: Model Development and Experimental Validation
BackgroundArtificial neural networks have achieved unprecedented success in the medical domain. This success depends on the availability of massive and representative datasets. However, data collection is often prevented by privacy concerns, and people want to take control ov...
Main Authors: | Shao, Rulin, He, Hongyu, Chen, Ziwei, Liu, Hui, Liu, Dianbo |
---|---|
Format: | Article |
Language: | English |
Published: |
JMIR Publications
2020-12-01
|
Series: | JMIR Formative Research |
Online Access: | http://formative.jmir.org/2020/12/e17265/ |
Similar Items
-
Stochastic Channel-Based Federated Learning With Neural Network Pruning for Medical Data Privacy Preservation: Model Development and Experimental Validation
by: Shao, Rulin, et al.
Published: (2021) -
Federated Neural Collaborative Filtering for privacy-preserving recommender systems
by: Langelaar, Johannes, et al.
Published: (2021) -
Preserving Privacy Against Side-Channel Leaks
by: Liu, Wen Ming
Published: (2014) -
Privacy preserving framework for federated learning in genomics
by: Kokje, Yashashree.
Published: (2021) -
Privacy-Preserving Unsupervised Domain Adaptation in Federated Setting
by: Lei Song, et al.
Published: (2020-01-01)