RVE-PFL: Robust Variational Encoder-Based Personalized Federated Learning Against Model Inversion Attacks-Reference-Cited by-同舟云学术

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RVE-PFL: Robust Variational Encoder-Based Personalized Federated Learning Against Model Inversion Attacks

Published:2024 Issue: Volume:19 Page:3772-3787
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Issa Wael¹^ORCID,Moustafa Nour¹^ORCID,Turnbull Benjamin²^ORCID,Choo Kim-Kwang Raymond³^ORCID

Affiliation:

1. ADFA, University of New South Wales, Canberra, ACT, Australia

2. Australian Centre for Cyber Security, University of New South Wales, Campbell, ACT, Australia

3. Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, USA

Funder

University of New South Wales (UNSW) Canberra

Australian Research Council’s Discovery Early Career Researcher Award

National Science Foundation (NSF) CREST

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/10206/10319981/10443672.pdf?arnumber=10443672

Reference46 articles.

1. Federated Learning for Internet of Things: A Comprehensive Survey

2. A Taxonomy of Attacks on Federated Learning

3. Blockchain-enabled Federated Learning: A Survey

4. ML-doctor: Holistic risk assessment of inference attacks against machine learning models;Liu

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Multifaceted Survey on Federated Learning: Fundamentals, Paradigm Shifts, Practical Issues, Recent Developments, Partnerships, Trade-Offs, Trustworthiness, and Ways Forward;IEEE Access;2024

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