Results

eNauka >  Rezultati >  Privacy-Preserving Deep Learning: A Survey on Theoretical Foundations, Software Frameworks, and Hardware Accelerators
Naziv: Privacy-Preserving Deep Learning: A Survey on Theoretical Foundations, Software Frameworks, and Hardware Accelerators
Autori: Jahns, Eric; Stojkov, Milan; Kinsy, Michel A
Godina: 2025
Publikacija: IEEE ACCESS
ISSN: 2169-3536 IEEE Access Pretraži identifikator
Tip rezultata: Naučni članak
Kolacija: vol. 13 str. 67821-67855
DOI: 10.1109/ACCESS.2025.3561721
WoS-ID: 001473151800042
Scopus-ID: 2-s2.0-105003697658
URI: https://enauka.gov.rs/handle/123456789/980733
Projekat: Department of Defense Scalable Asymmetric Lifecycle Engagement (SCALE) program
Izvor metapodataka: (Preuzeto iz Nasi u WoS)
M-kategorija: 
21M21 - Vodeći međunarodni časopis kategorije M21

3
SCOPUSTM
3
WEB OF SCIENCETM
Altmetric
Dimensions
Unpaywall

Items in eNauka are protected by copyright, with all rights reserved, unless otherwise indicated.