VerifyTL: Secure and Verifiable Collaborative Transfer Learning
Getting access to labeled datasets in certain sensitive application domains can be challenging. Hence, one may resort to transfer learning to transfer knowledge learned from a source domain with sufficient labeled data to a target domain with limited labeled data. However, most existing transfer lea...
Saved in:
Main Authors: | MA, Zhuoran, MA, Jianfeng, MIAO, Yinbin, LIU, Ximeng, ZHENG, Wei, CHOO, Kim-Kwang Raymond, DENG, Robert H. |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2023
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/7804 https://ink.library.smu.edu.sg/context/sis_research/article/8807/viewcontent/VerifyTL_av.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Verifiable data mining against malicious adversaries in industrial internet of things
by: MA, Zhuoran, et al.
Published: (2022) -
Secure and verifiable inference in deep neural networks
by: XU, Guowen, et al.
Published: (2020) -
Verifiable, fair and privacy-preserving broadcast authorization for flexible data sharing in clouds
by: SUN, Jianfei, et al.
Published: (2023) -
Vulnerability Analysis of RFID Protocols for Tag Ownership Transfer
by: PERIS-LOPEZ, Pedro, et al.
Published: (2010) -
Verifying parameterized timed security protocols
by: LI, Li, et al.
Published: (2015)