Understanding Adversarial Transferability in Federated Learning

• URL: http://arxiv.org/abs/2310.00616v1
• Date: Sun, 1 Oct 2023 08:35:46 GMT
• Title: Understanding Adversarial Transferability in Federated Learning
• Authors: Yijiang Li, Ying Gao and Haohan Wang
• Abstract summary: We investigate the robustness and security issues from a novel and practical setting. A group of malicious clients has impacted the model during training by disguising their identities and acting as benign clients. Our aim is to offer a full understanding of the challenges the FL system faces in this practical setting.
• Score: 16.204192821886927
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We investigate the robustness and security issues from a novel and practical setting: a group of malicious clients has impacted the model during training by disguising their identities and acting as benign clients, and only revealing their adversary position after the training to conduct transferable adversarial attacks with their data, which is usually a subset of the data that FL system is trained with. Our aim is to offer a full understanding of the challenges the FL system faces in this practical setting across a spectrum of configurations. We notice that such an attack is possible, but the federated model is more robust compared with its centralized counterpart when the accuracy on clean images is comparable. Through our study, we hypothesized the robustness is from two factors: the decentralized training on distributed data and the averaging operation. We provide evidence from both the perspective of empirical experiments and theoretical analysis. Our work has implications for understanding the robustness of federated learning systems and poses a practical question for federated learning applications.

Related papers

• Formal Logic-guided Robust Federated Learning against Poisoning Attacks [6.997975378492098]
  Federated Learning (FL) offers a promising solution to the privacy concerns associated with centralized Machine Learning (ML) FL is vulnerable to various security threats, including poisoning attacks, where adversarial clients manipulate the training data or model updates to degrade overall model performance. We present a defense mechanism designed to mitigate poisoning attacks in federated learning for time-series tasks.
  arXiv  Detail & Related papers  (2024-11-05T16:23:19Z)
• Fed-Credit: Robust Federated Learning with Credibility Management [18.349127735378048]
  Federated Learning (FL) is an emerging machine learning approach enabling model training on decentralized devices or data sources. We propose a robust FL approach based on the credibility management scheme, called Fed-Credit. The results exhibit superior accuracy and resilience against adversarial attacks, all while maintaining comparatively low computational complexity.
  arXiv  Detail & Related papers  (2024-05-20T03:35:13Z)
• Client-side Gradient Inversion Against Federated Learning from Poisoning [59.74484221875662]
  Federated Learning (FL) enables distributed participants to train a global model without sharing data directly to a central server. Recent studies have revealed that FL is vulnerable to gradient inversion attack (GIA), which aims to reconstruct the original training samples. We propose Client-side poisoning Gradient Inversion (CGI), which is a novel attack method that can be launched from clients.
  arXiv  Detail & Related papers  (2023-09-14T03:48:27Z)
• Mitigating Cross-client GANs-based Attack in Federated Learning [78.06700142712353]
  Multi distributed multimedia clients can resort to federated learning (FL) to jointly learn a global shared model. FL suffers from the cross-client generative adversarial networks (GANs)-based (C-GANs) attack. We propose Fed-EDKD technique to improve the current popular FL schemes to resist C-GANs attack.
  arXiv  Detail & Related papers  (2023-07-25T08:15:55Z)
• FedDefender: Client-Side Attack-Tolerant Federated Learning [60.576073964874]
  Federated learning enables learning from decentralized data sources without compromising privacy. It is vulnerable to model poisoning attacks, where malicious clients interfere with the training process. We propose a new defense mechanism that focuses on the client-side, called FedDefender, to help benign clients train robust local models.
  arXiv  Detail & Related papers  (2023-07-18T08:00:41Z)
• Defending Against Poisoning Attacks in Federated Learning with Blockchain [12.840821573271999]
  We propose a secure and reliable federated learning system based on blockchain and distributed ledger technology. Our system incorporates a peer-to-peer voting mechanism and a reward-and-slash mechanism, which are powered by on-chain smart contracts, to detect and deter malicious behaviors.
  arXiv  Detail & Related papers  (2023-07-02T11:23:33Z)
• 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)
• FL-Defender: Combating Targeted Attacks in Federated Learning [7.152674461313707]
  Federated learning (FL) enables learning a global machine learning model from local data distributed among a set of participating workers. FL is vulnerable to targeted poisoning attacks that negatively impact the integrity of the learned model. We propose textitFL-Defender as a method to combat FL targeted attacks.
  arXiv  Detail & Related papers  (2022-07-02T16:04:46Z)
• Robust Quantity-Aware Aggregation for Federated Learning [72.59915691824624]
  Malicious clients can poison model updates and claim large quantities to amplify the impact of their model updates in the model aggregation. Existing defense methods for FL, while all handling malicious model updates, either treat all quantities benign or simply ignore/truncate the quantities of all clients. We propose a robust quantity-aware aggregation algorithm for federated learning, called FedRA, to perform the aggregation with awareness of local data quantities.
  arXiv  Detail & Related papers  (2022-05-22T15:13:23Z)
• Acceleration of Federated Learning with Alleviated Forgetting in Local Training [61.231021417674235]
  Federated learning (FL) enables distributed optimization of machine learning models while protecting privacy. We propose FedReg, an algorithm to accelerate FL with alleviated knowledge forgetting in the local training stage. Our experiments demonstrate that FedReg not only significantly improves the convergence rate of FL, especially when the neural network architecture is deep.
  arXiv  Detail & Related papers  (2022-03-05T02:31:32Z)
• Towards Understanding Quality Challenges of the Federated Learning: A First Look from the Lens of Robustness [4.822471415125479]
  Federated learning (FL) aims to preserve users' data privacy while leveraging the entire dataset of all participants for training. FL still tends to suffer from quality issues such as attacks or byzantine faults. This paper investigates the effectiveness of state-of-the-art (SOTA) robust FL techniques in the presence of attacks and faults.
  arXiv  Detail & Related papers  (2022-01-05T02:06:39Z)

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.