Hercules: Boosting the Performance of Privacy-preserving Federated Learning-Reference-Cited by-同舟云学术

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Hercules: Boosting the Performance of Privacy-preserving Federated Learning

Published:2022 Issue: Volume: Page:1-18
ISSN:1545-5971
Container-title:IEEE Transactions on Dependable and Secure Computing
language:
Short-container-title:IEEE Trans. Dependable and Secure Comput.

Author:

Xu Guowen¹^ORCID,Han Xingshuo¹,Xu Shengmin²^ORCID,Zhang Tianwei¹^ORCID,Li Hongwei³^ORCID,Huang Xinyi²^ORCID,Deng Robert H.³^ORCID

Affiliation:

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

2. College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China

3. school of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/8858/4358699/09935302.pdf?arnumber=9935302

Cited by 8 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. Batch-Aggregate: Efficient Aggregation for Private Federated Learning in VANETs;IEEE Transactions on Dependable and Secure Computing;2024-09

2. Reconfigurable IoT Solution for Train Integrity and Monitoring;IEEE Internet of Things Journal;2024-06-15

3. Toward Efficient and Robust Federated Unlearning in IoT Networks;IEEE Internet of Things Journal;2024-06-15

4. Joint Top-K Sparsification and Shuffle Model for Communication-Privacy-Accuracy Tradeoffs in Federated-Learning-Based IoV;IEEE Internet of Things Journal;2024-06-01

5. A Robust Privacy-Preserving Federated Learning Model Against Model Poisoning Attacks;IEEE Transactions on Information Forensics and Security;2024

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