Honest Score Client Selection Scheme: Preventing Federated Learning Label Flipping Attacks in Non-IID Scenarios

• URL: http://arxiv.org/abs/2311.05826v1
• Date: Fri, 10 Nov 2023 02:07:41 GMT
• Title: Honest Score Client Selection Scheme: Preventing Federated Learning Label Flipping Attacks in Non-IID Scenarios
• Authors: Yanli Li, Huaming Chen, Wei Bao, Zhengmeng Xu, Dong Yuan,
• Abstract summary: Federated Learning (FL) is a promising technology that enables multiple actors to build a joint model without sharing their raw data. The distributed nature makes FL vulnerable to various poisoning attacks, including model poisoning attacks and data poisoning attacks. In this paper, we focus on the most representative data poisoning attack - "label flipping attack" and monitor its effectiveness when attacking the existing FL methods.
• Score: 27.36889020561564
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Learning (FL) is a promising technology that enables multiple actors to build a joint model without sharing their raw data. The distributed nature makes FL vulnerable to various poisoning attacks, including model poisoning attacks and data poisoning attacks. Today, many byzantine-resilient FL methods have been introduced to mitigate the model poisoning attack, while the effectiveness when defending against data poisoning attacks still remains unclear. In this paper, we focus on the most representative data poisoning attack - "label flipping attack" and monitor its effectiveness when attacking the existing FL methods. The results show that the existing FL methods perform similarly in Independent and identically distributed (IID) settings but fail to maintain the model robustness in Non-IID settings. To mitigate the weaknesses of existing FL methods in Non-IID scenarios, we introduce the Honest Score Client Selection (HSCS) scheme and the corresponding HSCSFL framework. In the HSCSFL, The server collects a clean dataset for evaluation. Under each iteration, the server collects the gradients from clients and then perform HSCS to select aggregation candidates. The server first evaluates the performance of each class of the global model and generates the corresponding risk vector to indicate which class could be potentially attacked. Similarly, the server evaluates the client's model and records the performance of each class as the accuracy vector. The dot product of each client's accuracy vector and global risk vector is generated as the client's host score; only the top p\% host score clients are included in the following aggregation. Finally, server aggregates the gradients and uses the outcome to update the global model. The comprehensive experimental results show our HSCSFL effectively enhances the FL robustness and defends against the "label flipping attack."

Related papers

• Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning [4.907460152017894]
  Federated Learning (FL) is a collaborative learning paradigm enabling participants to collectively train a shared machine learning model. Current FL defense strategies against data poisoning attacks either involve a trade-off between accuracy and robustness. We present FedZZ, which harnesses a zone-based deviating update (ZBDU) mechanism to effectively counter data poisoning attacks in FL.
  arXiv  Detail & Related papers  (2024-04-05T14:37:49Z)
• FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive Models [2.7539214125526534]
  Federated Learning (FL) thrives in training a global model with numerous clients. Recent research proposed poisoning attacks that cause a catastrophic loss in the accuracy of the global model. We propose FLGuard, a novel byzantine-robust FL method that detects malicious clients and discards malicious local updates.
  arXiv  Detail & Related papers  (2024-03-05T10:36:27Z)
• 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)
• Rethinking Client Drift in Federated Learning: A Logit Perspective [125.35844582366441]
  Federated Learning (FL) enables multiple clients to collaboratively learn in a distributed way, allowing for privacy protection. We find that the difference in logits between the local and global models increases as the model is continuously updated. We propose a new algorithm, named FedCSD, a Class prototype Similarity Distillation in a federated framework to align the local and global models.
  arXiv  Detail & Related papers  (2023-08-20T04:41:01Z)
• 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)
• No One Left Behind: Real-World Federated Class-Incremental Learning [111.77681016996202]
  Local-Global Anti-forgetting (LGA) model addresses local and global catastrophic forgetting. We develop a category-balanced gradient-adaptive compensation loss and a category gradient-induced semantic distillation loss. It augments perturbed prototype images of new categories collected from local clients via self-supervised prototype augmentation.
  arXiv  Detail & Related papers  (2023-02-02T06:41:02Z)
• FedCC: Robust Federated Learning against Model Poisoning Attacks [0.0]
  Federated Learning is designed to address privacy concerns in learning models. New distributed paradigm safeguards data privacy but differentiates the attack surface due to the server's inaccessibility to local datasets.
  arXiv  Detail & Related papers  (2022-12-05T01:52:32Z)
• FLCert: Provably Secure Federated Learning against Poisoning Attacks [67.8846134295194]
  We propose FLCert, an ensemble federated learning framework that is provably secure against poisoning attacks. Our experiments show that the label predicted by our FLCert for a test input is provably unaffected by a bounded number of malicious clients.
  arXiv  Detail & Related papers  (2022-10-02T17:50:04Z)
• 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)
• 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)

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.