Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning

• URL: http://arxiv.org/abs/2310.11594v3
• Date: Sun, 29 Jun 2025 19:25:01 GMT
• Title: Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning
• Authors: Taejin Kim, Jiarui Li, Shubhranshu Singh, Nikhil Madaan, Carlee Joe-Wong,
• Abstract summary: Federated learning enables the training of collaborative models without sharing of data.<n>This approach brings forth security challenges, notably poisoning and backdoor attacks.<n>We introduce Adversarial Robustness Unhardening (ARU), which is employed by a subset of adversarial clients.
• Score: 12.232863656375098
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The delicate equilibrium between user privacy and the ability to unleash the potential of distributed data is an important concern. Federated learning, which enables the training of collaborative models without sharing of data, has emerged as a privacy-centric solution. This approach brings forth security challenges, notably poisoning and backdoor attacks where malicious entities inject corrupted data into the training process, as well as evasion attacks that aim to induce misclassifications at test time. Our research investigates the intersection of adversarial training, a common defense method against evasion attacks, and backdoor attacks within federated learning. We introduce Adversarial Robustness Unhardening (ARU), which is employed by a subset of adversarial clients to intentionally undermine model robustness during federated training, rendering models susceptible to a broader range of evasion attacks. We present extensive experiments evaluating ARU's impact on adversarial training and existing robust aggregation defenses against poisoning and backdoor attacks. Our results show that ARU can substantially undermine adversarial training's ability to harden models against test-time evasion attacks, and that adversaries employing ARU can even evade robust aggregation defenses that often neutralize poisoning or backdoor attacks.

Related papers

• DROP: Poison Dilution via Knowledge Distillation for Federated Learning [23.793474308133003]
  Federated Learning is vulnerable to adversarial manipulation, where malicious clients can inject poisoned updates to influence the global model's behavior. We introduce DROP, a novel defense mechanism that combines clustering and activity-tracking techniques with extraction of benign behavior from clients. Our approach demonstrates superior robustness compared to existing defenses across a wide range of learning configurations.
  arXiv  Detail & Related papers  (2025-02-10T20:15:43Z)
• Client-Side Patching against Backdoor Attacks in Federated Learning [0.0]
  Federated learning is vulnerable to backdoor attacks launched by malicious participants. We propose a novel defense mechanism for federated learning systems designed to mitigate backdoor attacks on the clients-side. Our approach leverages adversarial learning techniques and model patching to neutralize the impact of backdoor attacks.
  arXiv  Detail & Related papers  (2024-12-13T23:17:10Z)
• FEDLAD: Federated Evaluation of Deep Leakage Attacks and Defenses [50.921333548391345]
  Federated Learning is a privacy preserving decentralized machine learning paradigm. Recent research has revealed that private ground truth data can be recovered through a gradient technique known as Deep Leakage. This paper introduces the FEDLAD Framework (Federated Evaluation of Deep Leakage Attacks and Defenses), a comprehensive benchmark for evaluating Deep Leakage attacks and defenses.
  arXiv  Detail & Related papers  (2024-11-05T11:42:26Z)
• 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)
• GANcrop: A Contrastive Defense Against Backdoor Attacks in Federated Learning [1.9632700283749582]
  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.
  arXiv  Detail & Related papers  (2024-05-31T09:33:16Z)
• 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)
• On the Difficulty of Defending Contrastive Learning against Backdoor Attacks [58.824074124014224]
  We show how contrastive backdoor attacks operate through distinctive mechanisms. Our findings highlight the need for defenses tailored to the specificities of contrastive backdoor attacks.
  arXiv  Detail & Related papers  (2023-12-14T15:54:52Z)
• 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)
• 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)
• Characterizing Internal Evasion Attacks in Federated Learning [12.873984200814533]
  Federated learning allows for clients to jointly train a machine learning model. Clients' models are vulnerable to attacks during the training and testing phases. In this paper, we address the issue of adversarial clients performing "internal evasion attacks"
  arXiv  Detail & Related papers  (2022-09-17T21:46:38Z)
• 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)
• Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges [10.177219272933781]
  Federated learning is a machine learning paradigm that emerges as a solution to the privacy-preservation demands in artificial intelligence. As machine learning, federated learning is threatened by adversarial attacks against the integrity of the learning model and the privacy of data via a distributed approach to tackle local and global learning.
  arXiv  Detail & Related papers  (2022-01-20T12:23:03Z)
• Certified Federated Adversarial Training [3.474871319204387]
  We tackle the scenario of securing FL systems conducting adversarial training when a quorum of workers could be completely malicious. We model an attacker who poisons the model to insert a weakness into the adversarial training such that the model displays apparent adversarial robustness. We show that this defence can preserve adversarial robustness even against an adaptive attacker.
  arXiv  Detail & Related papers  (2021-12-20T13:40:20Z)
• An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences [33.415612094924654]
  The goal of this paper is to review the different types of attacks and defences proposed so far. In a backdoor attack, the attacker corrupts the training data so to induce an erroneous behaviour at test time. Test time errors are activated only in the presence of a triggering event corresponding to a properly crafted input sample.
  arXiv  Detail & Related papers  (2021-11-16T13:06:31Z)
• Widen The Backdoor To Let More Attackers In [24.540853975732922]
  We investigate the scenario of a multi-agent backdoor attack, where multiple non-colluding attackers craft and insert triggered samples in a shared dataset. We discover a clear backfiring phenomenon: increasing the number of attackers shrinks each attacker's attack success rate. We then exploit this phenomenon to minimize the collective ASR of attackers and maximize defender's robustness accuracy.
  arXiv  Detail & Related papers  (2021-10-09T13:53:57Z)
• Where Did You Learn That From? Surprising Effectiveness of Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning [114.9857000195174]
  A major challenge to widespread industrial adoption of deep reinforcement learning is the potential vulnerability to privacy breaches. We propose an adversarial attack framework tailored for testing the vulnerability of deep reinforcement learning algorithms to membership inference attacks.
  arXiv  Detail & Related papers  (2021-09-08T23:44:57Z)
• 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.