Privacy-Preserving Collaborative Learning: A Scheme Providing Heterogeneous Protection-Reference-Cited by-同舟云学术

Privacy-Preserving Collaborative Learning: A Scheme Providing Heterogeneous Protection

Published:2024-01-15 Issue:2 Volume:11 Page:1840-1853
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Wang Xin¹^ORCID,Zhang Heng²^ORCID,Yang Ming¹^ORCID,Wu Xiaoming¹,Cheng Peng³

Affiliation:

1. Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, P. R. China

2. School of Computer Engineering, Jiangsu Ocean University, Lianyungang, China

3. State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China

Funder

Shandong Provincial Natural Science Foundation

Shandong Provincial Higher Education Youth Innovation Team Development Project

Taishan Scholars Program

“20 New Universities” Project of Jinan City

QLU/SDAS Pilot Project for Integrated Innovation of Science, Education, and Industry

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/10384482/10163938.pdf?arnumber=10163938

Reference26 articles.

1. Mobile Crowdsourcing in Smart Cities: Technologies, Applications, and Future Challenges

2. CrowdHMT: Crowd Intelligence With the Deep Fusion of Human, Machine, and IoT

3. Resilient Distributed Classification Learning Against Label Flipping Attack: An ADMM-Based Approach

4. Feature Inference Attack on Model Predictions in Vertical Federated Learning

5. Data Dissemination With Trajectory Privacy Protection for 6G-Oriented Vehicular Networks

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