Mitigating Adversarial Attacks in Federated Learning with Trusted Execution Environments

TL;DR

This paper introduces Pelta, a novel shielding mechanism using Trusted Execution Environments to mitigate adversarial attacks in federated learning, enhancing model robustness against various white-box attacks on datasets like CIFAR-10, CIFAR-100, and ImageNet.

Contribution

Pelta is the first TEE-based defense that masks the back-propagation chain to prevent adversarial sample crafting in federated learning.

Findings

Pelta effectively mitigates six state-of-the-art white-box adversarial attacks.

It demonstrates robustness on CIFAR-10, CIFAR-100, and ImageNet datasets.

First defense against Self-Attention Gradient attack in ensemble models.

Abstract

The main premise of federated learning (FL) is that machine learning model updates are computed locally to preserve user data privacy. This approach avoids by design user data to ever leave the perimeter of their device. Once the updates aggregated, the model is broadcast to all nodes in the federation. However, without proper defenses, compromised nodes can probe the model inside their local memory in search for adversarial examples, which can lead to dangerous real-world scenarios. For instance, in image-based applications, adversarial examples consist of images slightly perturbed to the human eye getting misclassified by the local model. These adversarial images are then later presented to a victim node's counterpart model to replay the attack. Typical examples harness dissemination strategies such as altered traffic signs (patch attacks) no longer recognized by autonomous vehicles or seemingly unaltered samples that poison the local dataset of the FL scheme to undermine its robustness. Pelta is a novel shielding mechanism leveraging Trusted Execution Environments (TEEs) that reduce the ability of attackers to craft adversarial samples. Pelta masks inside the TEE the first part of the back-propagation chain rule, typically exploited by attackers to craft the malicious samples. We evaluate Pelta on state-of-the-art accurate models using three well-established datasets: CIFAR-10, CIFAR-100 and ImageNet. We show the effectiveness of Pelta in mitigating six white-box state-of-the-art adversarial attacks, such as Projected Gradient Descent, Momentum Iterative Method, Auto Projected Gradient Descent, the Carlini & Wagner attack. In particular, Pelta constitutes the first attempt at defending an ensemble model against the Self-Attention Gradient attack to the best of our knowledge. Our code is available to the research community at https://github.com/queyrusi/Pelta.