LSFL: A Lightweight and Secure Federated Learning Scheme for Edge Computing-Reference-Cited by-同舟云学术

LSFL: A Lightweight and Secure Federated Learning Scheme for Edge Computing

Published:2023 Issue: Volume:18 Page:365-379
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Zhang Zhuangzhuang¹^ORCID,Wu Libing¹,Ma Chuanguo¹,Li Jianxin²^ORCID,Wang Jing³,Wang Qian¹^ORCID,Yu Shui⁴^ORCID

Affiliation:

1. Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China

2. School of Information Technology, Deakin University, Geelong, VIC, Australia

3. School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

4. School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Key Research and Development Plan of Hubei Province

Industry-University-Research Innovation Fund for Chinese Universities

Open Research Fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Safety, Risk, Reliability and Quality

Link

http://xplorestaging.ieee.org/ielx7/10206/9970396/09947081.pdf?arnumber=9947081

Reference52 articles.

1. Differentially private federated learning: A client level perspective;geyer;arXiv 1712 07557,2017

2. PPFL

3. Differentially Private Byzantine-Robust Federated Learning

4. Efficient, Private and Robust Federated Learning

5. $$\mathsf {FLOD}$$: Oblivious Defender for Private Byzantine-Robust Federated Learning with Dishonest-Majority

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