Memristor-Based Variation-Enabled Differentially Private Learning Systems for Edge Computing in IoT Academic Article

authors

• Smith, Scott

published proceedings

• IEEE INTERNET OF THINGS JOURNAL

author list (cited authors)

• Fu, J., Liao, Z., Liu, J., Smith, S. C., & Wang, J.

citation count

• 6

complete list of authors

• Fu, Jingyan||Liao, Zhiheng||Liu, Jianqing||Smith, Scott C||Wang, Jinhui

publication date

• January 2021

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Internet of Things  Journal

keywords

• Cycle-to-cycle Variation
• Data Privacy
• Differential Privacy (dp)
• Hardware
• Learning Systems
• Machine Learning
• Memristor
• Memristors
• Neural Network
• Noise Injection
• Privacy
• Training

Digital Object Identifier (DOI)

• 10.1109/JIOT.2020.3023623

start page

• 9672

end page

• 9682

volume

• 8

issue

• 12

URL

• http://dx.doi.org/10.1109/jiot.2020.3023623