Experimental results for the publication "Stealthy Backdoor Attack against Federated Learning through Frequency Domain by Backdoor Neuron Constraint and Model Camouflage"

DOI:10.4121/d671980c-4b9f-4f4c-bd95-3120d694990a.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/d671980c-4b9f-4f4c-bd95-3120d694990a

Datacite citation style

Qiao, Yanqi (2025): Experimental results for the publication "Stealthy Backdoor Attack against Federated Learning through Frequency Domain by Backdoor Neuron Constraint and Model Camouflage". Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/d671980c-4b9f-4f4c-bd95-3120d694990a.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

This research aims to investigate the vulnerabilities of existing federated learning frameworks against backdoor attacks and to develop a novel backdooring approach. The study focuses on advancing a new technology in this area. The data is produced by the research program, with all data being collected by scripts.

History

  • 2025-05-19 first online, published, posted

Publisher

4TU.ResearchData

Format

script/.py data/.txt

Organizations

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Cybersecurity

DATA

Files (2)