Anti-attack federated framework based on dynamic clustering and reputation evaluation

ZHANG Weiyi; LUO Qirui; ZHANG Lin

doi:10.11959/j.issn.2096-109x.2025069

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Anti-attack federated framework based on dynamic clustering and reputation evaluation

Papers | 更新时间：2026-01-28

- Anti-attack federated framework based on dynamic clustering and reputation evaluation
- Chinese Journal of Network and Information Security Vol. 11, Issue 6, Pages: 120-130(2025)
- 作者机构：
  
  南京邮电大学计算机学院，江苏南京 210003
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62372247;61872194);School-Level Natural Science Foundation of Nanjing University of Posts and Telecommunications(NY222142)
- DOI：10.11959/j.issn.2096-109x.2025069
  CLC： TP309
- Received：14 May 2025，
  
  Revised：2025-07-25，
  
  Published：25 December 2025
- 稿件说明：
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张唯一, 罗启瑞, 张琳. 基于动态聚类和信誉评估的抗攻击联邦框架[J]. 网络与信息安全学报, 2025, 11(6): 120-130.

ZHANG Weiyi, LUO Qirui, ZHANG Lin. Anti-attack federated framework based on dynamic clustering and reputation evaluation[J]. Chinese Journal of Network and Information Security, 2025, 11(6): 120-130.
张唯一, 罗启瑞, 张琳. 基于动态聚类和信誉评估的抗攻击联邦框架[J]. 网络与信息安全学报, 2025, 11(6): 120-130. DOI： 10.11959/j.issn.2096-109x.2025069.

ZHANG Weiyi, LUO Qirui, ZHANG Lin. Anti-attack federated framework based on dynamic clustering and reputation evaluation[J]. Chinese Journal of Network and Information Security, 2025, 11(6): 120-130. DOI： 10.11959/j.issn.2096-109x.2025069.

摘要

联邦学习在数据异构场景下的实际应用面临着严峻挑战，即恶意客户端的投毒攻击会降低全局模型性能。针对该挑战，提出了一种基于动态聚类和双维度信誉评估的鲁棒联邦学习框架动态聚类联邦学习（dynamic clustering federated learning

DCFL）。首先，DCFL采用无须预设聚类数量的HDBSCAN算法实现客户端自适应聚类，并基于相似性度量对噪声点进行重分配，将具有相似数据分布特征的客户端动态分组，避免将异构的良性客户端误判为恶意客户端。其次，在类簇内部，综合考察客户端模型更新相似度和损失一致性，利用双维度的互补指标综合评估客户端更新质量。最后，设计了动态阈值调整策略，基于信誉加权的簇内聚合策略有效强化了类簇的个性化功能。实验验证了DCFL在4种不同数据分布和安全环境下的性能优势，尤其是在高异构有攻击场景下，相比传统聚类联邦学习算法测试准确率提升了约9%，证明其在复杂联邦环境中具有良好的鲁棒性和适应性。

Abstract

The practical application of federated learning in scenarios with heterogeneous data faces a serious challenge

which is that poisoning attacks by malicious clients can degrade the global model. A robust federated learning framework DCFL (dynamic clustering federated learning) was proposed based on dynamic clustering and dual-dimensional reputation evaluation. Firstly

DCFL employed the HDBSCAN algorithm for adaptive client clustering without predefined cluster numbers

and redistributed noise points based on similarity measurements

dynamically grouping clients with similar data distribution characteristics

preventing misclassification of heterogeneous but benign clients as malicious. Secondly

within clusters

both model update similarity and loss consistency were examined comprehensively as complementary indicators to evaluate client update quality. Finally

a dynamic threshold adjustment strategy was designed

while reputation-weighted intra-cluster aggregation effectively enhances cluster personalization. Experiments validated DCFL's performance advantages across four different data distribution and security environments. Particularly in high-heterogeneity attack scenarios

DCFL improved testing accuracy by approximately 9 percentage points compared to traditional clustered federated learning algorithms

demonstrating its robustness and adaptability in complex federated environments.

关键词

Keywords

references

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