Gradients Stand-in for Defending Deep Leakage in Federated Learning

• URL: http://arxiv.org/abs/2410.08734v1
• Date: Fri, 11 Oct 2024 11:44:13 GMT
• Title: Gradients Stand-in for Defending Deep Leakage in Federated Learning
• Authors: H. Yi, H. Ren, C. Hu, Y. Li, J. Deng, X. Xie,
• Abstract summary: This study introduces a novel, efficacious method aimed at safeguarding against gradient leakage, namely, AdaDefense" This proposed approach not only effectively prevents gradient leakage, but also ensures that the overall performance of the model remains largely unaffected.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) has become a cornerstone of privacy protection, shifting the paradigm towards localizing sensitive data while only sending model gradients to a central server. This strategy is designed to reinforce privacy protections and minimize the vulnerabilities inherent in centralized data storage systems. Despite its innovative approach, recent empirical studies have highlighted potential weaknesses in FL, notably regarding the exchange of gradients. In response, this study introduces a novel, efficacious method aimed at safeguarding against gradient leakage, namely, ``AdaDefense". Following the idea that model convergence can be achieved by using different types of optimization methods, we suggest using a local stand-in rather than the actual local gradient for global gradient aggregation on the central server. This proposed approach not only effectively prevents gradient leakage, but also ensures that the overall performance of the model remains largely unaffected. Delving into the theoretical dimensions, we explore how gradients may inadvertently leak private information and present a theoretical framework supporting the efficacy of our proposed method. Extensive empirical tests, supported by popular benchmark experiments, validate that our approach maintains model integrity and is robust against gradient leakage, marking an important step in our pursuit of safe and efficient FL.

Related papers

• 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)
• Anti-Collapse Loss for Deep Metric Learning Based on Coding Rate Metric [99.19559537966538]
  DML aims to learn a discriminative high-dimensional embedding space for downstream tasks like classification, clustering, and retrieval. To maintain the structure of embedding space and avoid feature collapse, we propose a novel loss function called Anti-Collapse Loss. Comprehensive experiments on benchmark datasets demonstrate that our proposed method outperforms existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-03T13:44:20Z)
• Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation [2.28438857884398]
  This paper introduces an advanced approach for fortifying Federated Learning (FL) systems against label-flipping attacks. We propose a consensus-based verification process integrated with an adaptive thresholding mechanism. Our results indicate a significant mitigation of label-flipping attacks, bolstering the FL system's resilience.
  arXiv  Detail & Related papers  (2024-03-05T20:54:56Z)
• A Theoretical Insight into Attack and Defense of Gradient Leakage in Transformer [11.770915202449517]
  The Deep Leakage from Gradient (DLG) attack has emerged as a prevalent and highly effective method for extracting sensitive training data by inspecting exchanged gradients. This research presents a comprehensive analysis of the gradient leakage method when applied specifically to transformer-based models.
  arXiv  Detail & Related papers  (2023-11-22T09:58:01Z)
• GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization [52.55628139825667]
  Federated Learning (FL) has emerged as a promising distributed machine learning framework to preserve clients' privacy. Recent studies find that an attacker can invert the shared gradients and recover sensitive data against an FL system by leveraging pre-trained generative adversarial networks (GAN) as prior knowledge. We propose textbfGradient textbfInversion over textbfFeature textbfDomains (GIFD), which disassembles the GAN model and searches the feature domains of the intermediate layers.
  arXiv  Detail & Related papers  (2023-08-09T04:34:21Z)
• Gradient Leakage Defense with Key-Lock Module for Federated Learning [14.411227689702997]
  Federated Learning (FL) is a widely adopted privacy-preserving machine learning approach. Recent findings reveal that privacy may be compromised and sensitive information potentially recovered from shared gradients. We propose a new gradient leakage defense technique that secures arbitrary model architectures using a private key-lock module.
  arXiv  Detail & Related papers  (2023-05-06T16:47:52Z)
• Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning [28.76786159247595]
  gradient leakage attacks exploit clients' uploaded gradients to reconstruct their sensitive data. In this paper, we explore a novel defensive paradigm that departs from conventional gradient perturbation approaches. We design Refiner that jointly optimize two metrics for privacy protection and performance maintenance.
  arXiv  Detail & Related papers  (2022-12-05T05:36:15Z)
• 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)
• Query-Efficient Black-box Adversarial Attacks Guided by a Transfer-based Prior [50.393092185611536]
  We consider the black-box adversarial setting, where the adversary needs to craft adversarial examples without access to the gradients of a target model. Previous methods attempted to approximate the true gradient either by using the transfer gradient of a surrogate white-box model or based on the feedback of model queries. We propose two prior-guided random gradient-free (PRGF) algorithms based on biased sampling and gradient averaging.
  arXiv  Detail & Related papers  (2022-03-13T04:06:27Z)
• Do Gradient Inversion Attacks Make Federated Learning Unsafe? [70.0231254112197]
  Federated learning (FL) allows the collaborative training of AI models without needing to share raw data. Recent works on the inversion of deep neural networks from model gradients raised concerns about the security of FL in preventing the leakage of training data. In this work, we show that these attacks presented in the literature are impractical in real FL use-cases and provide a new baseline attack.
  arXiv  Detail & Related papers  (2022-02-14T18:33:12Z)
• FedBoosting: Federated Learning with Gradient Protected Boosting for Text Recognition [7.988454173034258]
  Federated Learning (FL) framework allows learning a shared model collaboratively without data being centralized or shared among data owners. We show in this paper that the generalization ability of the joint model is poor on Non-Independent and Non-Identically Distributed (Non-IID) data. We propose a novel boosting algorithm for FL to address both the generalization and gradient leakage issues.
  arXiv  Detail & Related papers  (2020-07-14T18:47:23Z)

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.