PA-iMFL: Communication-Efficient Privacy Amplification Method Against Data Reconstruction Attack in Improved Multilayer Federated Learning-Reference-Cited by-同舟云学术

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PA-iMFL: Communication-Efficient Privacy Amplification Method Against Data Reconstruction Attack in Improved Multilayer Federated Learning

Published:2024-05-15 Issue:10 Volume:11 Page:17960-17974
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Wang Jianhua¹^ORCID,Chang Xiaolin¹^ORCID,Mišić Jelena²^ORCID,Mišić Vojislav B.²^ORCID,Chen Zhi³^ORCID,Fan Junchao¹^ORCID

Affiliation:

1. Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, China

2. Department of Computer Science, Toronto Metropolitan University, Toronto, ON, Canada

3. Beijing Institute of Computer Technology and Application, Beijing, China

Funder

National Natural Science Foundation of China

Natural Science and Engineering Research Council (NSERC) of Canada

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488907/10528217/10416901.pdf?arnumber=10416901

Reference58 articles.

1. Improving the accuracy of medical diagnosis with causal machine learning

2. Machine Learning-Powered Encrypted Network Traffic Analysis: A Comprehensive Survey

3. How the GDPR Will Change the World

4. Communication-efficient learning of deep networks from decentralized data;McMahan

5. Distance-Aware Hierarchical Federated Learning in Blockchain-Enabled Edge Computing Network

Cited by 2 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. Towards Well-trained Model Robustness in Federated Learning: An Adversarial- Example-Generation- Efficiency Perspective;ICC 2024 - IEEE International Conference on Communications;2024-06-09

2. 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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