Depriving the Survival Space of Adversaries Against Poisoned Gradients in Federated Learning-Reference-Cited by-同舟云学术

Depriving the Survival Space of Adversaries Against Poisoned Gradients in Federated Learning

Published:2024 Issue: Volume:19 Page:5405-5418
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Lu Jianrong¹^ORCID,Hu Shengshan¹^ORCID,Wan Wei¹^ORCID,Li Minghui²^ORCID,Zhang Leo Yu³^ORCID,Xue Lulu¹,Jin Hai¹^ORCID

Affiliation:

1. National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Laboratory, Hubei Key Laboratory of Distributed System Security, Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

2. School of Software Engineering, Huazhong University of Science and Technology, Wuhan, China

3. School of Information and Communication Technology, Griffith University, Nathan, QLD, Australia

Funder

National Natural Science Foundation of China

Hubei Province Key Research and Development Technology Special Innovation Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/10206/10319981/10417061.pdf?arnumber=10417061

Reference44 articles.

1. How to backdoor federated learning;Bagdasaryan

2. A little is enough: Circumventing defenses for distributed learning;Baruch

3. Machine learning with adversaries: Byzantine tolerant gradient descent;Blanchard

4. Differentially private secure multi-party computation for federated learning in financial applications

5. FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping