A Model Consistency-Based Countermeasure to GAN-Based Data Poisoning Attack in Federated Learning

TL;DR

This paper introduces a novel defense mechanism called Model Consistency-Based Defense (MCD) to detect stealthy GAN-based data poisoning attacks in federated learning, along with a new attack model VagueGAN, demonstrating improved detection and robustness across multiple datasets.

Contribution

The paper proposes MCD, a new model consistency-based defense, and VagueGAN, a GAN-based attack model, enhancing detection of stealthy poisoned data in federated learning.

Findings

MCD effectively detects various poisoned data, including stealthy GAN-based attacks.

VagueGAN generates realistic yet poisoned data with high stealthiness.

Experiments show improved robustness of federated learning against data poisoning.

Abstract

In federated learning (FL), although the original intention of available but not visible data is to allay data privacy concerns, it potentially brings new security threats, particularly poisoning attacks that target such not visible local data. Intuitively, such data poisoning attacks have great potential in stealthily degrading global FL outcomes, and are expected to be even stealthier if being enhanced by generative models like generative adversarial networks (GANs). However, existing defense methods have not been thoroughly challenged in this regard and generally fail to be aware of a local generation of seemingly legitimate poisoned data. With a growing concern on potentially stealthier attacks, in this paper, a cost-effective defense mechanism named Model Consistency-Based Defense (MCD) is proposed, which offers a comprehensive examination of available local models across multiple feature dimensions, providing an indirect yet effective means of identifying hidden data poisoning attackers. To push the limit of MCD against stealthier attacks, we propose a new GAN-based data poisoning attack model named VagueGAN and an unsupervised variant of it, which can be flexibly deployed to generate seemingly legitimate but noisy poisoned data. The consistency of GAN outputs revealed by VagueGAN helps strengthen MCD to work against stealthier GAN-based attacks as well as other mainstream ones. Extensive experiments on multiple open datasets (MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100, and Mini-Imagenet) indicate that our attack method better balances the trade-off between attack effectiveness and stealthiness with low complexity. More importantly, our defense mechanism is shown to be more competent in identifying a variety of poisoned data, particularly stealthier GAN-poisoned ones.