Sparse and Stealthy: A Hybrid Byzantine Attack Exploiting Neural Network Topology

Core Concepts

The authors propose a novel hybrid Byzantine attack that combines a sparse yet aggressive attack targeting sensitive neural network weights with a stealthy but accumulating attack, which together form a strong yet imperceptible attack against various defense mechanisms in federated learning.

Abstract

The authors argue that existing Byzantine attacks often focus on being either aggressive or imperceptible, but not both. To address this, they propose a novel hybrid Byzantine attack that combines two components:

A sparse yet aggressive attack: This part of the attack targets only certain sensitive weights in the neural network with higher perturbations, aiming to bypass defenses that rely on index-wise outlier detection.

A stealthy but accumulating attack: This part of the attack applies smaller perturbations across many weights, accumulating over time to undermine the model's performance, while remaining imperceptible to defenses that rely on geometric distance-based outlier detection.

The authors leverage insights from neural network pruning to identify the sensitive weights to target with the aggressive part of the attack. They show through extensive simulations that this hybrid approach is effective against a wide range of defense mechanisms, reducing test accuracy by up to 60% in IID settings and completely diverging the model in non-IID settings.

Stats

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Quotes

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Key Insights Distilled From

Aggressive or Imperceptible, or Both

by Emre Ozfatur... at arxiv.org 04-10-2024

https://arxiv.org/pdf/2404.06230.pdf

Deeper Inquiries

How can the proposed hybrid attack be extended to target specific tasks or classes, rather than just degrading overall model performance

The proposed hybrid attack can be extended to target specific tasks or classes by incorporating task-specific or class-specific information into the attack strategy. One approach could be to analyze the data distribution across different tasks or classes and design the attack to focus on perturbing the model parameters that are more critical for those specific tasks or classes. This targeted approach can be achieved by modifying the sparsity mask generation process to prioritize certain weights or neurons that are more relevant to the tasks or classes of interest. By customizing the attack in this way, the hybrid attack can be tailored to degrade the performance of the model on specific tasks or classes while minimizing the impact on others.

What are the potential limitations or drawbacks of relying on neural network pruning techniques to identify sensitive weights for the aggressive part of the attack

Relying on neural network pruning techniques to identify sensitive weights for the aggressive part of the attack may have some limitations and drawbacks. One potential limitation is that neural network pruning techniques are typically designed to improve model efficiency and reduce computational complexity by removing redundant or less important weights. However, these techniques may not always accurately identify the most sensitive weights for the attack, as the criteria for pruning are based on different objectives. Additionally, neural network pruning may not consider the specific vulnerabilities of the model to adversarial attacks, which could result in the attack targeting suboptimal weights or neurons. Furthermore, the reliance on pruning techniques may introduce additional computational overhead and complexity to the attack design process, especially when dealing with large and complex neural networks.

Can the principles behind this hybrid attack design be applied to other distributed learning settings beyond federated learning

The principles behind the hybrid attack design can be applied to other distributed learning settings beyond federated learning by adapting the attack strategy to the specific characteristics and requirements of the target setting. In distributed learning scenarios where multiple parties collaborate to train a shared model, similar challenges related to Byzantine attacks and model poisoning may arise. By incorporating the concept of a hybrid attack that combines imperceptible and aggressive components, tailored to the defense mechanisms and network topology of the specific distributed learning setting, the attack strategy can be effectively applied to enhance the robustness of the collaborative learning process. Additionally, the sparsity-based approach and the consideration of task-specific or class-specific information can be generalized to different distributed learning frameworks to improve the security and reliability of the trained models.