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https://github.com/warisgill/feddefender
FedDefender is a novel defense mechanism designed to safeguard Federated Learning from the poisoning attacks (i.e., backdoor attacks).
https://github.com/warisgill/feddefender
backdoor-attacks data differential-testing federated-learning poisoning-attack
Last synced: 19 days ago
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FedDefender is a novel defense mechanism designed to safeguard Federated Learning from the poisoning attacks (i.e., backdoor attacks).
- Host: GitHub
- URL: https://github.com/warisgill/feddefender
- Owner: warisgill
- License: mit
- Created: 2023-07-22T05:36:48.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-06T09:06:30.000Z (4 months ago)
- Last Synced: 2024-10-12T16:36:42.255Z (about 1 month ago)
- Topics: backdoor-attacks, data, differential-testing, federated-learning, poisoning-attack
- Language: Python
- Homepage: https://warisgill.github.io/FedDefender/
- Size: 37.1 KB
- Stars: 9
- Watchers: 1
- Forks: 3
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# FedDefender: Backdoor Attack Defense in Federated Learning (Tutorial)
`This tutorial is based on a paper accepted at SE4SafeML: Dependability and Trustworthiness of Safety-Critical Systems with Machine Learned Components (Colocated with FSE 2023). The ArXiv version of the manuscript is available` [here](https://arxiv.org/abs/2307.08672).
For any questions regarding FedDefender's artifact, please direct them to Waris Gill at [email protected].