Client selection for federated learning against label flipping attacks

LI Jianxin; CHEN Siguang

doi:10.11959/j.issn.2096-3750.2025.00403

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Client selection for federated learning against label flipping attacks

Theory and Technology | 更新时间：2026-01-26

- Client selection for federated learning against label flipping attacks
- Chinese Journal on Internet of Things Vol. 9, Issue 3, Pages: 170-179(2025)
- 作者机构：
  
  南京邮电大学物联网学院，江苏南京 210003
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61971235);333 High-Level Talents Training Project of Jiangsu Province, 1311 Talents Plan of NJUPT
- DOI：10.11959/j.issn.2096-3750.2025.00403
  CLC： TP393
- Received：01 November 2023，
  
  Revised：2024-06-30，
  
  Published：10 September 2025
- 稿件说明：
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李建鑫,陈思光.面向联邦学习标签翻转攻击的客户端选择防御方法[J].物联网学报,2025,09(03):170-179.

LI Jianxin,CHEN Siguang.Client selection for federated learning against label flipping attacks[J].Chinese Journal on Internet of Things,2025,09(03):170-179.
李建鑫,陈思光.面向联邦学习标签翻转攻击的客户端选择防御方法[J].物联网学报,2025,09(03):170-179. DOI： 10.11959/j.issn.2096-3750.2025.00403.

LI Jianxin,CHEN Siguang.Client selection for federated learning against label flipping attacks[J].Chinese Journal on Internet of Things,2025,09(03):170-179. DOI： 10.11959/j.issn.2096-3750.2025.00403.

摘要

联邦学习允许多个客户端仅共享模型更新而不上传本地数据以协作训练一个全局模型，但正是由于它这种基于分布式的全局聚合模式，导致联邦学习易受到标签翻转攻击的恶意影响。为此，提出了一种面向联邦学习标签翻转攻击的客户端选择防御算法。具体地，该算法基于客户端与辅助客户端模型的余弦相似度以及客户端模型的准确率获得每个客户端的可靠因子，并依据可靠因子进行加权聚合，以此获得全局模型。通过赋予良性客户端更高的权重，可显著降低恶性客户端对全局模型的影响，提高模型的准确率。结合客户端的历史良性情况，融合汤普森采样方法，计算每个客户端被选择进行聚合的概率，确定下一轮参与聚合的客户端。通过筛选更加良性的客户端进行聚合可有效防御标签翻转攻击，提升模型鲁棒性。仿真结果表明，与现有的联邦平均（FedAvg

federated averaging）算法和通过信任引导的拜占庭鲁棒联邦学习（FLTrust

Byzantine-robust federated learning via trust bootstrapping）算法相比，该算法能够更有效地防御标签翻转攻击并获得更高的准确率。

Abstract

Federated learning (FL) allows multiple clients to train a global model collaboratively by sharing only model updates without uploading local data. But due to its distributed global aggregation mode

FL is vulnerable to the malicious impact of label flipping attacks. Therefore

a client selection algorithm was proposed for FL against label flipping attacks. Specifically

the algorithm obtains the reliability score of each client based on the cosine similarity of client model and auxiliary client model and the accuracy of client model

and carries out weighted aggregation according to the reliability score to obtain the global model. By assigning higher weights to benign clients

the influence of malicious clients on the global model can be significantly reduced and the accuracy of the model can be improved. Then

Thompson sampling method was integrated to calculate the probability of each client being selected for aggregation and determine the clients participating in the aggregation in the next round based on the historical benign data of the clients. By screening more benign clients for aggregation

label flipping attacks can be effectively prevented and the robustness of the model was improved. Simulation results show that compared with the existing FedAvg and FLTrust algorithms

the proposed algorithm can defend against label flipping attacks more effectively and achieve higher accuracy.

关键词

Keywords

references

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