RAB$^2$-DEF: Dynamic and explainable defense against adversarial attacks in Federated Learning to fair poor clients

• URL: http://arxiv.org/abs/2410.08244v2
• Date: Wed, 16 Apr 2025 07:54:52 GMT
• Title: RAB$^2$-DEF: Dynamic and explainable defense against adversarial attacks in Federated Learning to fair poor clients
• Abstract summary: Federated Learning is proposed as a solution to data privacy concerns derived from different source data scenarios.<n>We propose RAB$2$-DEF, a $textbfr$esilient $textbfa$gainst $textbfbtextyzantine$ and $textbfb$ackdoor attacks.<n>We test the performance of RAB$2$-DEF in image datasets and both byzantine and backdoor attacks considering the state-of-the-art defenses.
• Score: 8.627236956385325
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: At the same time that artificial intelligence is becoming popular, concern and the need for regulation is growing, including among other requirements the data privacy. In this context, Federated Learning is proposed as a solution to data privacy concerns derived from different source data scenarios due to its distributed learning. The defense mechanisms proposed in literature are just focused on defending against adversarial attacks and the performance, leaving aside other important qualities such as explainability, fairness to poor quality clients, dynamism in terms of attacks configuration and generality in terms of being resilient against different kinds of attacks. In this work, we propose RAB$^2$-DEF, a $\textbf{r}$esilient $\textbf{a}$gainst $\textbf{b}\text{yzantine}$ and $\textbf{b}$ackdoor attacks which is $\textbf{d}$ynamic, $\textbf{e}$xplainable and $\textbf{f}$air to poor clients using local linear explanations. We test the performance of RAB$^2$-DEF in image datasets and both byzantine and backdoor attacks considering the state-of-the-art defenses and achieve that RAB$^2$-DEF is a proper defense at the same time that it boosts the other qualities towards trustworthy artificial intelligence.

Related papers

• AI Security in the Foundation Model Era: A Comprehensive Survey from a Unified Perspective [12.53973720107764]
  We propose a closed-loop threat taxonomy that frames model-data interactions along four axes.<n>The resulting four classes of security threats represent distinct but interrelated categories of attacks.<n>Our framework establishes a foundation for developing scalable, transferable, and cross-modal security strategies.
  arXiv  Detail & Related papers  (2026-03-25T22:53:43Z)
• FAROS: Robust Federated Learning with Adaptive Scaling against Backdoor Attacks [9.466036066320946]
  backdoor attacks pose a significant threat to Federated Learning (FL)<n>We propose FAROS, an enhanced FL framework that incorporates Adaptive Differential Scaling (ADS) and Robust Core-set Computing (RCC)<n>RCC effectively mitigates the risk of single-point failure by computing the centroid of a core set comprising clients with the highest confidence.
  arXiv  Detail & Related papers  (2026-01-05T06:55:35Z)
• Generalization under Byzantine & Poisoning Attacks: Tight Stability Bounds in Robust Distributed Learning [11.915183759106412]
  Two primary threat models have been studied: Byzantine attacks and data poisoning attacks.<n>We show that Byzantine attacks are intrinsically more harmful to generalization than data poisoning.
  arXiv  Detail & Related papers  (2025-06-22T12:59:15Z)
• TabAttackBench: A Benchmark for Adversarial Attacks on Tabular Data [10.068187951387415]
  Adrial attacks induce incorrect predictions through imperceptible perturbations to input data.<n>This study introduces a benchmark that evaluates adversarial attacks on datasets with respect to both effectiveness and imperceptibility.<n>The benchmark findings offer actionable insights for designing more imperceptible adversarial attacks.
  arXiv  Detail & Related papers  (2025-05-27T11:01:32Z)
• ELBA-Bench: An Efficient Learning Backdoor Attacks Benchmark for Large Language Models [55.93380086403591]
  Generative large language models are vulnerable to backdoor attacks. $textitELBA-Bench$ allows attackers to inject backdoor through parameter efficient fine-tuning. $textitELBA-Bench$ provides over 1300 experiments.
  arXiv  Detail & Related papers  (2025-02-22T12:55:28Z)
• Deep Adversarial Defense Against Multilevel-Lp Attacks [5.604868766260297]
  This paper introduces a computationally efficient multilevel $ell_p$ defense, called the Efficient Robust Mode Connectivity (EMRC) method. Similar to analytical continuation approaches used in continuous optimization, the method blends two $p$-specific adversarially optimal models. We present experiments demonstrating that our approach performs better on various attacks as compared to AT-$ell_infty$, E-AT, and MSD.
  arXiv  Detail & Related papers  (2024-07-12T13:30:00Z)
• Towards Attack-tolerant Federated Learning via Critical Parameter Analysis [85.41873993551332]
  Federated learning systems are susceptible to poisoning attacks when malicious clients send false updates to the central server. This paper proposes a new defense strategy, FedCPA (Federated learning with Critical Analysis) Our attack-tolerant aggregation method is based on the observation that benign local models have similar sets of top-k and bottom-k critical parameters, whereas poisoned local models do not.
  arXiv  Detail & Related papers  (2023-08-18T05:37:55Z)
• Adversarial Attacks Neutralization via Data Set Randomization [3.655021726150369]
  Adversarial attacks on deep learning models pose a serious threat to their reliability and security. We propose a new defense mechanism that is rooted on hyperspace projection. We show that our solution increases the robustness of deep learning models against adversarial attacks.
  arXiv  Detail & Related papers  (2023-06-21T10:17:55Z)
• 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)
• IDEA: Invariant Defense for Graph Adversarial Robustness [60.0126873387533]
  We propose an Invariant causal DEfense method against adversarial Attacks (IDEA) We derive node-based and structure-based invariance objectives from an information-theoretic perspective. Experiments demonstrate that IDEA attains state-of-the-art defense performance under all five attacks on all five datasets.
  arXiv  Detail & Related papers  (2023-05-25T07:16:00Z)
• The Best Defense is a Good Offense: Adversarial Augmentation against Adversarial Attacks [91.56314751983133]
  $A5$ is a framework to craft a defensive perturbation to guarantee that any attack towards the input in hand will fail. We show effective on-the-fly defensive augmentation with a robustifier network that ignores the ground truth label. We also show how to apply $A5$ to create certifiably robust physical objects.
  arXiv  Detail & Related papers  (2023-05-23T16:07:58Z)
• Hindering Adversarial Attacks with Implicit Neural Representations [25.422201099331637]
  Lossy Implicit Network Activation Coding (LINAC) defence successfully hinders several common adversarial attacks. We devise a Parametric Bypass Approximation (PBA) attack strategy for key-based defences, which successfully invalidates an existing method in this category.
  arXiv  Detail & Related papers  (2022-10-22T13:10:24Z)
• Defending against the Label-flipping Attack in Federated Learning [5.769445676575767]
  Federated learning (FL) provides autonomy and privacy by design to participating peers. The label-flipping (LF) attack is a targeted poisoning attack where the attackers poison their training data by flipping the labels of some examples. We propose a novel defense that first dynamically extracts those gradients from the peers' local updates.
  arXiv  Detail & Related papers  (2022-07-05T12:02:54Z)
• Fabricated Flips: Poisoning Federated Learning without Data [9.060263645085564]
  Attacks on Federated Learning (FL) can severely reduce the quality of the generated models. We propose a data-free untargeted attack (DFA) that synthesizes malicious data to craft adversarial models. DFA achieves similar or even higher attack success rate than state-of-the-art untargeted attacks.
  arXiv  Detail & Related papers  (2022-02-07T20:38:28Z)
• Adaptive Feature Alignment for Adversarial Training [56.17654691470554]
  CNNs are typically vulnerable to adversarial attacks, which pose a threat to security-sensitive applications. We propose the adaptive feature alignment (AFA) to generate features of arbitrary attacking strengths. Our method is trained to automatically align features of arbitrary attacking strength.
  arXiv  Detail & Related papers  (2021-05-31T17:01:05Z)
• Towards Variable-Length Textual Adversarial Attacks [68.27995111870712]
  It is non-trivial to conduct textual adversarial attacks on natural language processing tasks due to the discreteness of data. In this paper, we propose variable-length textual adversarial attacks(VL-Attack) Our method can achieve $33.18$ BLEU score on IWSLT14 German-English translation, achieving an improvement of $1.47$ over the baseline model.
  arXiv  Detail & Related papers  (2021-04-16T14:37:27Z)
• Composite Adversarial Attacks [57.293211764569996]
  Adversarial attack is a technique for deceiving Machine Learning (ML) models. In this paper, a new procedure called Composite Adrial Attack (CAA) is proposed for automatically searching the best combination of attack algorithms. CAA beats 10 top attackers on 11 diverse defenses with less elapsed time.
  arXiv  Detail & Related papers  (2020-12-10T03:21:16Z)
• Attack Agnostic Adversarial Defense via Visual Imperceptible Bound [70.72413095698961]
  This research aims to design a defense model that is robust within a certain bound against both seen and unseen adversarial attacks. The proposed defense model is evaluated on the MNIST, CIFAR-10, and Tiny ImageNet databases. The proposed algorithm is attack agnostic, i.e. it does not require any knowledge of the attack algorithm.
  arXiv  Detail & Related papers  (2020-10-25T23:14:26Z)
• Robust and Accurate Authorship Attribution via Program Normalization [24.381734600088453]
  Source code attribution approaches have achieved remarkable accuracy thanks to the rapid advances in deep learning. In particular, they can be easily deceived by adversaries who attempt to either create a forgery of another author or to mask the original author. We present a novel learning framework, $textitnormalize-and-predict$ ($textitN&P$), that in theory guarantees the robustness of any authorship-attribution approach.
  arXiv  Detail & Related papers  (2020-07-01T21:27:38Z)

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.