A Robust Privacy-Preserving Federated Learning Model Against Model Poisoning Attacks-Reference-Cited by-同舟云学术

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A Robust Privacy-Preserving Federated Learning Model Against Model Poisoning Attacks

Published:2024 Issue: Volume:19 Page:6693-6708
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Yazdinejad Abbas¹^ORCID,Dehghantanha Ali¹^ORCID,Karimipour Hadis²^ORCID,Srivastava Gautam³^ORCID,Parizi Reza M.⁴^ORCID

Affiliation:

1. Cyber Science Lab, Canada Cyber Foundry, University of Guelph, Guelph, ON, Canada

2. Department of Electrical and Software Engineering, School of Engineering, University of Calgary, Calgary, AB, Canada

3. Department of Mathematics and Computer Science, Brandon University, Brandon, MB, Canada

4. Decentralized Science Lab, Kennesaw State University, Kennesaw, GA, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/10206/10319981/10574838.pdf?arnumber=10574838

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1. A dynamic anonymization privacy-preserving model based on hierarchical sequential three-way decisions;Information Sciences;2025-01

2. GA-mADAM-IIoT: A new lightweight threats detection in the industrial IoT via genetic algorithm with attention mechanism and LSTM on multivariate time series sensor data;Sensors International;2025

3. Evading Cyber-Attacks on Hadoop Ecosystem: A Novel Machine Learning-Based Security-Centric Approach towards Big Data Cloud;Information;2024-09-10

4. An Efficient Greedy Hierarchical Federated Learning Training Method Based on Trusted Execution Environments;Electronics;2024-09-06

5. Machine Learning for Blockchain and IoT Systems in Smart Cities: A Survey;Future Internet;2024-09-06

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