Improved Distributed Backdoor Attacks in Federated Learning by Density-Adaptive Data Poisoning and Projection-Based Gradient Updating

Improved Distributed Backdoor Attacks in Federated Learning by Density-Adaptive Data Poisoning and Projection-Based Gradient Updating

While federated learning enables collaborative model training with preserved data locality, it remains vulnerable to evolving backdoor attacks that exploit its distributed architecture. Compared with centralized backdoor attacks, a distributed backdoor attack (DBA) poses a greater threat to FL syste...

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Bibliographic Details
Main Authors: Jian Wang, Hong Shen, Wei Ke, Xue Hua Liu
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Adversarial machine learning
anomaly detection
cybersecurity
data poisoning
density-adaptive
distributed backdoor attacks
Online Access:https://ieeexplore.ieee.org/document/11072142/
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https://ieeexplore.ieee.org/document/11072142/

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