Privacy-Preserving Federated Learning Using Homomorphic Encryption
Privacy-Preserving Federated Learning Using Homomorphic Encryption
About this item
Full title
Author / Creator
Publisher
Basel: MDPI AG
Journal title
Language
English
Formats
Publication information
Publisher
Basel: MDPI AG
Subjects
More information
Scope and Contents
Contents
Federated learning (FL) is a machine learning technique that enables distributed devices to train a learning model collaboratively without sharing their local data. FL-based systems can achieve much stronger privacy preservation since the distributed devices deliver only local model parameters trained with local data to a centralized server. Howeve...
Alternative Titles
Full title
Privacy-Preserving Federated Learning Using Homomorphic Encryption
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_4ee03b38b9874c8b8b112cdac3e75c10
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_4ee03b38b9874c8b8b112cdac3e75c10
Other Identifiers
ISSN
2076-3417
E-ISSN
2076-3417
DOI
10.3390/app12020734
