Can Quantum Federated Learning Withstand Circuit-Level Backdoors?

• URL: http://arxiv.org/abs/2605.27416v1
• Date: Mon, 18 May 2026 12:36:36 GMT
• Title: Can Quantum Federated Learning Withstand Circuit-Level Backdoors?
• Abstract summary: Quantum Federated Learning (QFL) inherits the core vulnerability of federated optimization to malicious clients.<n>This work proposes a novel CircUit-Level backdoor Threat (CULT) model that formalizes four stealthy attacks.
• Score: 1.4470144741753357
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum Federated Learning (QFL) inherits the core vulnerability of federated optimization to malicious clients, while also introducing an attack surface from variational circuit training and measurement-driven gradients. This work proposes a novel CircUit-Level backdoor Threat (CULT) model that formalizes four stealthy attacks by exploiting quantum-aware mechanisms, including Grover, Pauli, Bit-flip, and Sign-flip. By enabling malicious clients on both in-training and post-training surfaces, these attacks can critically undermine the learning process. We establish a rigorous theoretical foundation to demonstrate attack stealthiness under standard smoothness assumptions. Experiments on the MNIST and CIFAR-10 datasets with non-IID splits and varying fractions of malicious clients show that even a single malicious client can induce severe accuracy degradation under FedAvg aggregation. While popular defenses, including Krum, Multi-Krum, FoolsGold, FLGuardian, and Mud-HoG, reduce degradation in many regimes, they fail to eliminate worst-case failure cases, where accuracy drops up to 50\%. The experimental analysis further reveals that under the CULT model, malicious updates effectively mask their presence by staying close to benign norms, thereby helping attackers evade detection.

Related papers

• FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks [11.27557330957057]
  We introduce FlipGuard, a proactive defense framework that selectively perturbs model weights prior to quantization.<n>By breaking the adversary's precise alignment between weight patterns and quantization boundaries, FlipGuard suppresses backdoor activation without requiring access to training data or trigger samples.
  arXiv  Detail & Related papers  (2026-06-27T15:02:42Z)
• SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors [9.273743530407977]
  Federated Learning (FL) enables collaborative model training without sharing raw data.<n>It is vulnerable to backdoor attacks, where malicious clients embed hidden triggers into local training data to manipulate model predictions.<n>We propose SCRUB-FL, a two-phase solution for post-training backdoor removal in FL.
  arXiv  Detail & Related papers  (2026-06-21T22:34:34Z)
• Detecting and Mitigating Backdoor Attacks in OTA-FL Systems: A Two-Stage Robust Aggregation Scheme [27.75838619796163]
  Over-the-air federated learning (OTA-FL) improves communication efficiency by exploiting the superposition property of wireless channels.<n>This creates a critical security vulnerability: the parameter server cannot access individual local updates.<n>We propose a two-stage robust aggregation framework for defending against backdoor attacks in OTA-FL.
  arXiv  Detail & Related papers  (2026-05-19T01:57:31Z)
• Robust Anti-Backdoor Instruction Tuning in LVLMs [53.766434746801366]
  We introduce a lightweight, certified-agnostic defense framework for large visual language models (LVLMs)<n>Our framework finetunes only adapter modules and text embedding layers under instruction tuning.<n>Experiments against seven attacks on Flickr30k and MSCOCO demonstrate that ours reduces their attack success rate to nearly zero.
  arXiv  Detail & Related papers  (2025-06-04T01:23:35Z)
• Efficient Backdoor Defense in Multimodal Contrastive Learning: A Token-Level Unlearning Method for Mitigating Threats [52.94388672185062]
  We propose an efficient defense mechanism against backdoor threats using a concept known as machine unlearning. This entails strategically creating a small set of poisoned samples to aid the model's rapid unlearning of backdoor vulnerabilities. In the backdoor unlearning process, we present a novel token-based portion unlearning training regime.
  arXiv  Detail & Related papers  (2024-09-29T02:55:38Z)
• Unlearning Backdoor Threats: Enhancing Backdoor Defense in Multimodal Contrastive Learning via Local Token Unlearning [49.242828934501986]
  Multimodal contrastive learning has emerged as a powerful paradigm for building high-quality features. backdoor attacks subtly embed malicious behaviors within the model during training. We introduce an innovative token-based localized forgetting training regime.
  arXiv  Detail & Related papers  (2024-03-24T18:33:15Z)
• One-bit Flip is All You Need: When Bit-flip Attack Meets Model Training [54.622474306336635]
  A new weight modification attack called bit flip attack (BFA) was proposed, which exploits memory fault inject techniques. We propose a training-assisted bit flip attack, in which the adversary is involved in the training stage to build a high-risk model to release.
  arXiv  Detail & Related papers  (2023-08-12T09:34:43Z)
• G$^2$uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering [116.4277292854053]
  Federated Learning (FL) offers collaborative model training without data sharing. FL is vulnerable to backdoor attacks, where poisoned model weights lead to compromised system integrity. We present G$2$uardFL, a protective framework that reinterprets the identification of malicious clients as an attributed graph clustering problem.
  arXiv  Detail & Related papers  (2023-06-08T07:15:04Z)
• FedGrad: Mitigating Backdoor Attacks in Federated Learning Through Local Ultimate Gradients Inspection [3.3711670942444014]
  Federated learning (FL) enables multiple clients to train a model without compromising sensitive data. The decentralized nature of FL makes it susceptible to adversarial attacks, especially backdoor insertion during training. We propose FedGrad, a backdoor-resistant defense for FL that is resistant to cutting-edge backdoor attacks.
  arXiv  Detail & Related papers  (2023-04-29T19:31:44Z)
• FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning [66.56240101249803]
  We study how hardening benign clients can affect the global model (and the malicious clients) We propose a trigger reverse engineering based defense and show that our method can achieve improvement with guarantee robustness. Our results on eight competing SOTA defense methods show the empirical superiority of our method on both single-shot and continuous FL backdoor attacks.
  arXiv  Detail & Related papers  (2022-10-23T22:24:03Z)
• CrowdGuard: Federated Backdoor Detection in Federated Learning [39.58317527488534]
  This paper presents a novel defense mechanism, CrowdGuard, that effectively mitigates backdoor attacks in Federated Learning. CrowdGuard employs a server-located stacked clustering scheme to enhance its resilience to rogue client feedback. The evaluation results demonstrate that CrowdGuard achieves a 100% True-Positive-Rate and True-Negative-Rate across various scenarios.
  arXiv  Detail & Related papers  (2022-10-14T11:27:49Z)
• Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design [76.29738151117583]
  Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients. However, low quality models could be uploaded to the aggregator server by unreliable clients, leading to a degradation or even a collapse of training. We model these unreliable behaviors of clients and propose a defensive mechanism to mitigate such a security risk.
  arXiv  Detail & Related papers  (2021-05-10T08:02:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.