Privacy-enhanced knowledge transfer with collaborative split learning over teacher ensembles

Privacy-enhanced knowledge transfer with collaborative split learning over teacher ensembles

Knowledge Transfer has received much attention for its ability to transfer knowledge, rather than data, from one application task to another. In order to comply with the stringent data privacy regulations, privacy-preserving knowledge transfer is highly desirable. The Private Aggregation of Teacher...

Full description

Bibliographic Details
Main Authors: Liu, Ziyao, Guo, Jiale, Yang, Mengmeng, Yang, Wenzhuo, Fan, Jiani, Lam, Kwok-Yan
Other Authors: School of Computer Science and Engineering
Format: Conference Paper
Language:English
Published: 2023
Subjects:
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Privacy-Preservation
Knowledge Transfer
Online Access:https://hdl.handle.net/10356/172524

Internet

https://hdl.handle.net/10356/172524

Similar Items