Fed2VAEs: An Efficient Privacy-Preserving Federated Learning Approach Based on Variational Autoencoders-Reference-Cited by-同舟云学术

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Fed2VAEs: An Efficient Privacy-Preserving Federated Learning Approach Based on Variational Autoencoders

Published:2024-06-09 Issue: Volume:32 Page:2634-2639
ISSN:
Container-title:ICC 2024 - IEEE International Conference on Communications
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Author:

Liu Jianqi¹,Luo Xiangyang¹,Zhao Zhiwei¹,Chang Zheng¹,Pan Miao²,Li Pan³,Min Geyong⁴,Li Huiyong¹

Affiliation:

1. University of Electronic Science and Technology of China,China

2. University of Houston,Department of Electrical and Computer Engineering,USA

3. Computer and Systems Engineering, Case Western Reserve University,Department of Electrical,USA

4. University of Exeter,Department of Computer Science,UK

Funder

National Natural Science Foundation of China

Natural Science Foundation of Sichuan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10622104/10622158/10622682.pdf?arnumber=10622682

Reference19 articles.

1. Internet of Things (IoT) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions and Research Directions

2. Lightweight Federated Learning for Large-scale IoT Devices with Privacy Guarantee

3. Communication-efficient learning of deep networks from decentralized data;McMahan;Artificial intelligence and statistics,2017

4. Gradient Leakage Attacks in Federated Learning: Research Frontiers, Taxonomy and Future Directions

5. Deep leakage from gradients;Zhu;Advances in neural information processing systems,2019

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