GANcrop: A Contrastive Defense Against Backdoor Attacks in Federated Learning

• URL: http://arxiv.org/abs/2405.20727v1
• Date: Fri, 31 May 2024 09:33:16 GMT
• Title: GANcrop: A Contrastive Defense Against Backdoor Attacks in Federated Learning
• Authors: Xiaoyun Gan, Shanyu Gan, Taizhi Su, Peng Liu,
• Abstract summary: This paper introduces a novel defense mechanism against backdoor attacks in federated learning, named GANcrop. Experimental findings demonstrate that GANcrop effectively safeguards against backdoor attacks, particularly in non-IID scenarios.
• Score: 1.9632700283749582
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With heightened awareness of data privacy protection, Federated Learning (FL) has attracted widespread attention as a privacy-preserving distributed machine learning method. However, the distributed nature of federated learning also provides opportunities for backdoor attacks, where attackers can guide the model to produce incorrect predictions without affecting the global model training process. This paper introduces a novel defense mechanism against backdoor attacks in federated learning, named GANcrop. This approach leverages contrastive learning to deeply explore the disparities between malicious and benign models for attack identification, followed by the utilization of Generative Adversarial Networks (GAN) to recover backdoor triggers and implement targeted mitigation strategies. Experimental findings demonstrate that GANcrop effectively safeguards against backdoor attacks, particularly in non-IID scenarios, while maintaining satisfactory model accuracy, showcasing its remarkable defensive efficacy and practical utility.

Related papers

• DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learning [4.932796168357307]
  Federated Learning (FL) enables collaborative model training across distributed devices while preserving local data privacy, making it ideal for mobile and embedded systems. However, the decentralized nature of FL also opens vulnerabilities to model poisoning attacks, particularly backdoor attacks. We propose DeTrigger, a scalable and efficient backdoor-robust federated learning framework.
  arXiv  Detail & Related papers  (2024-11-19T04:12:14Z)
• 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)
• Celtibero: Robust Layered Aggregation for Federated Learning [0.0]
  We introduce Celtibero, a novel defense mechanism that integrates layered aggregation to enhance robustness against adversarial manipulation. We demonstrate that Celtibero consistently achieves high main task accuracy (MTA) while maintaining minimal attack success rates (ASR) across a range of untargeted and targeted poisoning attacks.
  arXiv  Detail & Related papers  (2024-08-26T12:54:00Z)
• 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)
• BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning [85.2564206440109]
  This paper reveals the threats in this practical scenario that backdoor attacks can remain effective even after defenses. We introduce the emphtoolns attack, which is resistant to backdoor detection and model fine-tuning defenses.
  arXiv  Detail & Related papers  (2023-11-20T02:21:49Z)
• Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning [13.12397828096428]
  Adversarial Robustness Unhardening (ARU) is employed by a subset of adversaries to intentionally undermine model robustness during decentralized training. We present empirical experiments evaluating ARU's impact on adversarial training and existing robust aggregation defenses against poisoning and backdoor attacks.
  arXiv  Detail & Related papers  (2023-10-17T21:38:41Z)
• Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations [55.2480439325792]
  Federated Learning (FL) facilitates decentralized machine learning model training, preserving data privacy, lowering communication costs, and boosting model performance through diversified data sources. FL faces vulnerabilities such as poisoning attacks, undermining model integrity with both untargeted performance degradation and targeted backdoor attacks. We define a new notion of strong adaptive adversaries, capable of adapting to multiple objectives simultaneously. MESAS is the first defense robust against strong adaptive adversaries, effective in real-world data scenarios, with an average overhead of just 24.37 seconds.
  arXiv  Detail & Related papers  (2023-06-06T11:44:42Z)
• On the Vulnerability of Backdoor Defenses for Federated Learning [8.345632941376673]
  Federated Learning (FL) is a popular distributed machine learning paradigm that enables jointly training a global model without sharing clients' data. In this paper, we study whether the current defense mechanisms truly neutralize the backdoor threats from federated learning. We propose a new federated backdoor attack method for possible countermeasures.
  arXiv  Detail & Related papers  (2023-01-19T17:02:02Z)
• On the Effectiveness of Adversarial Training against Backdoor Attacks [111.8963365326168]
  A backdoored model always predicts a target class in the presence of a predefined trigger pattern. In general, adversarial training is believed to defend against backdoor attacks. We propose a hybrid strategy which provides satisfactory robustness across different backdoor attacks.
  arXiv  Detail & Related papers  (2022-02-22T02:24:46Z)
• Identifying Backdoor Attacks in Federated Learning via Anomaly Detection [31.197488921578984]
  Federated learning is vulnerable to backdoor attacks. This paper proposes an effective defense against the attack by examining shared model updates. We demonstrate through extensive analyses that our proposed methods effectively mitigate state-of-the-art backdoor attacks.
  arXiv  Detail & Related papers  (2022-02-09T07:07:42Z)
• Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning [51.15273664903583]
  Data heterogeneity has been identified as one of the key features in federated learning but often overlooked in the lens of robustness to adversarial attacks. This paper focuses on characterizing and understanding its impact on backdooring attacks in federated learning through comprehensive experiments using synthetic and the LEAF benchmarks.
  arXiv  Detail & Related papers  (2021-02-01T06:06:21Z)

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.