A Secure and Robust Knowledge Transfer Framework via Stratified-Causality Distribution Adjustment in Intelligent Collaborative Services-Reference-Cited by-同舟云学术

A Secure and Robust Knowledge Transfer Framework via Stratified-Causality Distribution Adjustment in Intelligent Collaborative Services

Published:2024-01 Issue:1 Volume:73 Page:58-72
ISSN:0018-9340
Container-title:IEEE Transactions on Computers
language:
Short-container-title:IEEE Trans. Comput.

Author:

Jia Ju¹^ORCID,Ma Siqi²^ORCID,Wang Lina³^ORCID,Liu Yang⁴^ORCID,Deng Robert H.⁵^ORCID

Affiliation:

1. School of Cyber Science and Engineering, Southeast University, Nanjing, China

2. School of System and Computing, University of New South Wales, Canberra, ACT, Australia

3. Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China

4. School of Computer Science and Engineering, Nanyang Technological University, Singapore

5. School of Information Systems, Singapore Management University, Singapore

Funder

National Natural Science Foundation of China

Start-up Research Fund of Southeast University

National Research Foundation, SingaporeDSO National LaboratoriesAI Singapore Programme

National Research Foundation, SingaporeCyber Security AgencyNational Cybersecurity R&D Programme

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Hardware and Architecture,Theoretical Computer Science,Software

Link

http://xplorestaging.ieee.org/ielx7/12/10372122/10261341.pdf?arnumber=10261341

Reference45 articles.

1. Scenario-based AI Benchmark Evaluation of Distributed Cloud/Edge Computing Systems

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3. Automated Customization of On-Device Inference for Quality-of-Experience Enhancement

4. Learning to Generalize: Meta-Learning for Domain Generalization

5. Universal Domain Adaptation