Adversarial Variational Graph Representation for Stealthy Model Poisoning Attacks on Federated Learning

Core Concepts

The proposed VGAE-MP attack leverages an adversarial variational graph autoencoder to generate malicious local models solely based on the overheard benign local models, without requiring access to the training data. This enables the attack to effectively compromise the global model in federated learning while remaining stealthy and undetectable.

Abstract

The paper presents a new data-untethered model poisoning (VGAE-MP) attack on federated learning (FL). The attack leverages an adversarial variational graph autoencoder (VGAE) to create malicious local models based solely on the overheard benign local models, without any access to the training data. Key highlights: The VGAE-MP attack extracts the graph structural correlations among the benign local models and the training data features, and then adversarially regenerates the graph structure to generate malicious local models. A new attacking algorithm is developed to train the malicious local models using the VGAE and sub-gradient descent, while enabling an optimal selection of the benign local models for training the VGAE. Experiments demonstrate that the proposed VGAE-MP attack can gradually degrade the FL accuracy and bypass the detection of existing defense mechanisms, posing a severe threat to FL. The attack is more effective on the FashionMNIST and CIFAR-10 datasets compared to the MNIST dataset, due to the higher complexity and variance in the former datasets.

Stats

The number of benign devices (I) increases from 5 to 30. The number of attackers (J) increases from 1 to 5. The global model is trained with 100 communication rounds. The local model is trained with 10 iterations. The number of selected model parameters (M) is set to 100, 200, or 300.

Quotes

"The proposed VGAE-MP attack extends an adversarial variational graph autoencoder (VGAE) to create malicious local models based solely on the benign local models overheard without any access to the training data of FL." "VGAE-MP attack extracts graph structural correlations among the benign local models and the training data features, adversarially regenerates the graph structure, and generates malicious local models using the adversarial graph structure and benign models' features."

Key Insights Distilled From

Leverage Variational Graph Representation For Model Poisoning on Federated Learning

by Kai Li,Xin Y... at arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.15042.pdf

Deeper Inquiries

How can the proposed VGAE-MP attack be extended to other distributed learning paradigms beyond federated learning

The proposed VGAE-MP attack can be extended to other distributed learning paradigms beyond federated learning by adapting the attack strategy to the specific characteristics of each paradigm. For example: Decentralized Learning: In decentralized learning, multiple nodes collaborate to train a global model without a central server. The VGAE-MP attack could be modified to target the communication between nodes and manipulate the model updates exchanged in the decentralized setting. Multi-Party Computation (MPC): In MPC, multiple parties jointly compute a function while keeping their inputs private. The VGAE-MP attack could be adapted to inject malicious inputs or manipulate the computation process to compromise the final result. Transfer Learning: In transfer learning, knowledge from one task is transferred to another related task. The VGAE-MP attack could be used to poison the transfer learning process by injecting biased or misleading information during the knowledge transfer phase. By customizing the VGAE-MP attack to suit the communication and collaboration patterns of different distributed learning paradigms, adversaries can undermine the integrity and accuracy of the learning process in various settings.

What are the potential countermeasures or defense mechanisms that can effectively detect and mitigate the VGAE-MP attack without significantly impacting the performance of federated learning

Countermeasures and defense mechanisms to detect and mitigate the VGAE-MP attack in federated learning include: Anomaly Detection: Implement anomaly detection algorithms to identify unusual patterns in the model updates that may indicate the presence of malicious local models generated by the VGAE-MP attack. Model Verification: Verify the integrity of the received model updates by comparing them with the expected updates based on the training data, ensuring that no unauthorized modifications have been made. Secure Aggregation: Use secure aggregation techniques to aggregate the model updates in a way that preserves privacy and prevents attackers from manipulating the global model through malicious local models. Adversarial Training: Incorporate adversarial training methods to train the global model to be robust against attacks like the VGAE-MP, by exposing the model to adversarial examples during training. These defense mechanisms can help in detecting and mitigating the VGAE-MP attack without significantly impacting the performance of federated learning, ensuring the integrity and security of the collaborative learning process.

What are the implications of the VGAE-MP attack on the broader landscape of adversarial machine learning, and how can the insights from this work inform the development of more robust and secure distributed learning systems

The VGAE-MP attack highlights the vulnerability of federated learning systems to sophisticated model poisoning attacks that can compromise the accuracy and integrity of the learning process. The implications of this attack on the broader landscape of adversarial machine learning include: Increased Threat Surface: The VGAE-MP attack demonstrates the potential for adversaries to exploit the collaborative nature of distributed learning systems to launch targeted attacks that evade detection and manipulate the learning outcomes. Need for Robust Defenses: The insights from the VGAE-MP attack underscore the importance of developing robust defense mechanisms that can detect and mitigate adversarial attacks in distributed learning settings, safeguarding the privacy and accuracy of the learning process. Advancing Security Research: The VGAE-MP attack serves as a case study for researchers and practitioners to explore new techniques and strategies for securing distributed learning systems against sophisticated adversarial threats, driving innovation in adversarial machine learning defense. By understanding the implications of the VGAE-MP attack and leveraging the insights gained from this work, the development of more secure and resilient distributed learning systems can be informed, enhancing the overall security posture of collaborative machine learning environments.