Secure aggregation for semi-decentralized federated learning under heterogeneous data

HUANG Mei; WANG Lingling; ZHANG Zhengyin; LIU Yufei; SUN Yitong

doi:10.11959/j.issn.2096-109x.2025029

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Secure aggregation for semi-decentralized federated learning under heterogeneous data

Papers | 更新时间：2025-06-30

- Secure aggregation for semi-decentralized federated learning under heterogeneous data
- Chinese Journal of Network and Information Security Vol. 11, Issue 3, Pages: 175-189(2025)
- 作者机构：
  
  青岛科技大学信息科学技术学院，山东青岛 266042
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61802217);The Natural Science Foundation of Shandong Province(ZR2023MF082);Qingdao Science and Technology Program Key Research and Development Project(22-3-4-xxgg-10-gx)
- DOI：10.11959/j.issn.2096-109x.2025029
  CLC： TP309.2
- Received：31 October 2024，
  
  Revised：2025-03-30，
  
  Published：25 June 2025
- 稿件说明：
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黄梅, 王玲玲, 张政胤, 等. 面向异构数据的半分布式联邦学习安全聚合[J]. 网络与信息安全学报, 2025, 11(3): 175-189.

Format: HUANG M, WANG L L, ZHANG Z Y, et al. Secure aggregation for semi-decentralized federated learning under heterogeneous data[J]. Chinese Journal of Network and Information Security, 2025, 11(3): 175-189.
黄梅, 王玲玲, 张政胤, 等. 面向异构数据的半分布式联邦学习安全聚合[J]. 网络与信息安全学报, 2025, 11(3): 175-189. DOI： 10.11959/j.issn.2096-109x.2025029.

Format: HUANG M, WANG L L, ZHANG Z Y, et al. Secure aggregation for semi-decentralized federated learning under heterogeneous data[J]. Chinese Journal of Network and Information Security, 2025, 11(3): 175-189. DOI： 10.11959/j.issn.2096-109x.2025029.

摘要

半分布式联邦学习结合集中式和分布式的优势，提高了系统的可扩展性和灵活性。然而，开放异构的联邦学习架构下，半诚实参与者和异构本地数据的存在加剧了模型聚合过程中节点隐私泄露和模型难收敛的问题。一方面，节点间复杂的邻接关系增加了本地数据隐私泄露的风险，节点数据的隐私保护难以得到有效保障。另一方面，本地模型之间的差异随着本地数据异构程度的增大而增大，进而减缓了全局模型收敛速度，降低了全局模型聚合的准确性。为解决上述问题，提出了一种面向异构数据的半分布式联邦学习安全聚合方案，在实现节点隐私保护的同时提升全局模型收敛效率。基于交替方向乘子法设计随机掩蔽机制，在保证模型正确聚合的前提下避免任何实体访问单个节点的本地模型，增强了本地数据的隐私保护。此外，提出了一种双重加权聚合策略，基于全局模型在不同节点样本上的损失设计节点簇内聚合权重，根据不同分簇对全局模型的贡献为其设计全局聚合权重。在3个公共标准数据集上进行的综合实验表明，与先进的方案相比，提升了数据非独立同分布时的模型收敛速度和准确率。

Abstract

Semi-decentralized federated learning has gained attention for combining the advantages of centralized and decentralized approaches

thereby enhancing system scalability and flexibility. However

in the open heterogeneous federated learning architecture

the existence of semi-honest participants and heterogeneous data has aggravated the problems of node privacy leakage and difficult model convergence during model aggregation. The differences between local models have increased with the degree of local data heterogeneity

which has further slowed down the global model’ convergence and reduced its accuracy. Additionally

the complex network topology among nodes has heightened the risk of local data privacy leakage

making it difficult to ensure local data privacy protection. To tackle these issues

a secure aggregation scheme for heterogeneous data under a semi-distributed federated learning architecture was designed. This scheme aimed to enhance the global model’s convergence performance while safeguarding node privacy. A random masking mechanism was developed based on the alternating direction multiplier method. This mechanism strengthened local data privacy protection and prevented access to individual node models. Moreover

a double-weighted aggregation strategy was proposed. In this strategy

intra-cluster aggregation weights for nodes were determined based on the loss of the global model on different node samples

and global aggregation weights for different clusters were established according to their contribution to the global model. Extensive experiments were carried out on three public standard datasets. The results demonstrate that

compared with advanced schemes

the proposed method improves model convergence speed and accuracy in the context of heterogeneous data.

关键词

Keywords

references

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