A4FL: Federated Adversarial Defense via Adversarial Training and Pruning Against Backdoor Attack

A4FL: Federated Adversarial Defense via Adversarial Training and Pruning Against Backdoor Attack

Backdoor attacks threaten federated learning (FL) models, where malicious participants embed hidden triggers into local models during training. These triggers can compromise crucial applications, such as autonomous systems, when they activate specific inputs, causing a targeted misclassification in...

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Bibliographic Details
Main Authors: Saeed-Uz-Zaman, Bin Li, Muhammad Hamid, Muhammad Saleem, Mohammed Aman
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Backdoor attack
adversarial training
universal adversarial perturbations (UAPs)
backdoor defense
Online Access:https://ieeexplore.ieee.org/document/10992684/
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https://ieeexplore.ieee.org/document/10992684/

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