Shield Against Gradient Leakage Attacks: Adaptive Privacy-Preserving Federated Learning-Reference-Cited by-同舟云学术

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Shield Against Gradient Leakage Attacks: Adaptive Privacy-Preserving Federated Learning

Published:2023 Issue: Volume: Page:1-16
ISSN:1063-6692
Container-title:IEEE/ACM Transactions on Networking
language:
Short-container-title:IEEE/ACM Trans. Networking

Author:

Hu Jiahui¹^ORCID,Wang Zhibo¹^ORCID,Shen Yongsheng²,Lin Bohan¹,Sun Peng³^ORCID,Pang Xiaoyi⁴^ORCID,Liu Jian¹^ORCID,Ren Kui¹^ORCID

Affiliation:

1. School of Cyber Science and Technology, Zhejiang University, Hangzhou, China

2. Hangzhou City Brain Company Ltd, Hangzhou, China

3. College of Computer Science and Electronic Engineering, Hunan University, Changsha, China

4. School of Cyber Science and Engineering, Wuhan University, Wuhan, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Key Research and Development Program of Zhejiang

Hangzhou Leading Innovation and Entrepreneurship Team

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Computer Science Applications,Software

Link

http://xplorestaging.ieee.org/ielx7/90/4359146/10264857.pdf?arnumber=10264857

Cited by 3 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 Socially Optimal Data Marketplace With Differentially Private Federated Learning;IEEE/ACM Transactions on Networking;2024-06

2. Towards Efficient Asynchronous Federated Learning in Heterogeneous Edge Environments;IEEE INFOCOM 2024 - IEEE Conference on Computer Communications;2024-05-20

3. Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning;IEEE INFOCOM 2024 - IEEE Conference on Computer Communications;2024-05-20

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