Privacy-preserving federated learning based on partial low-quality data
Privacy-preserving federated learning based on partial low-quality data
About this item
Full title
Author / Creator
Wang, Huiyong , Wang, Qi , Ding, Yong , Tang, Shijie and Wang, Yujue
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
Formats
Publication information
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Subjects
More information
Scope and Contents
Contents
Traditional machine learning requires collecting data from participants for training, which may lead to malicious acquisition of privacy in participants’ data. Federated learning provides a method to protect participants’ data privacy by transferring the training process from a centralized server to terminal devices. However, the server may still o...
Alternative Titles
Full title
Privacy-preserving federated learning based on partial low-quality data
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_a72580171ad449d585042614cba14198
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_a72580171ad449d585042614cba14198
Other Identifiers
ISSN
2192-113X
E-ISSN
2192-113X
DOI
10.1186/s13677-024-00618-8
