DP-FedDRC：缓解维度坍塌的差分隐私个性化联邦学习

王民利; 张宇浩; 胡昌慧

doi:10.11959/j.issn.2096-109x.2026008

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DP-FedDRC：缓解维度坍塌的差分隐私个性化联邦学习

学术论文 | 更新时间：2026-05-26

- DP-FedDRC：缓解维度坍塌的差分隐私个性化联邦学习
- DP-FedDRC: differentially private personalized federated learning to alleviate dimension collapse
- 网络与信息安全学报 2026年12卷第1期页码：104-116
- 作者机构：
  
  海南大学网络空间安全学院（密码学院），海南海口 570228
- 作者简介：
  
  王民利（2000- ），男，山西运城人，海南大学硕士生，主要研究方向为联邦学习、隐私保护。
  张宇浩（1998- ），男，安徽宿州人，海南大学博士生，主要研究方向为安全多方计算、隐私保护机器学习。
  胡昌慧（1985- ），男，山东莱西人，海南大学教授，博士生导师，主要研究方向为安全多方计算、差分隐私和隐私保护机器学习。
- 基金信息：
  
  国家自然科学基金(62262012);中国国家留学基金委资助项目(202407560059);海南省自然科学基金(124QN176);海南省研究生创新科研课题(Qhyb2024-02);海南大学科研启动基金(KYQD22094)
- DOI：10.11959/j.issn.2096-109x.2026008
  中图分类号： TP309
- 收稿：2025-07-30，
  
  修回：2025-11-09，
  
  纸质出版：2026-02-25
- 稿件说明：
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王民利, 张宇浩, 胡昌慧. DP-FedDRC：缓解维度坍塌的差分隐私个性化联邦学习[J]. 网络与信息安全学报, 2026, 12(1): 104-116.

Wang Minli, Zhang Yuhao, Hu Changhui. DP-FedDRC: differentially private personalized federated learning to alleviate dimension collapse[J]. Chinese Journal of Network and Information Security, 2026, 12(1): 104-116.
王民利, 张宇浩, 胡昌慧. DP-FedDRC：缓解维度坍塌的差分隐私个性化联邦学习[J]. 网络与信息安全学报, 2026, 12(1): 104-116. DOI： 10.11959/j.issn.2096-109x.2026008.

Wang Minli, Zhang Yuhao, Hu Changhui. DP-FedDRC: differentially private personalized federated learning to alleviate dimension collapse[J]. Chinese Journal of Network and Information Security, 2026, 12(1): 104-116. DOI： 10.11959/j.issn.2096-109x.2026008.

摘要

联邦学习在分布式隐私保护场景中面临数据异质性与隐私泄露双重挑战。个性化联邦学习通过约束局部模型更新方向缓解数据分布差异，而差分隐私则提供严格的隐私保障。然而，现有方法在同时解决这两个问题时，未能充分考量噪声对数据非独立同分布场景下联邦学习训练过程的耦合影响，导致隐私-效用权衡不足。实验表明，差分隐私噪声会显著加剧数据异质性引发的维度坍塌问题。该问题导致特征空间的有效自由度衰减，并削弱了特征的判别性表达能力，成为模型性能下降的关键原因。为此，提出一种融合特征去相关与特征残差约束的差分隐私个性化联邦学习框架（DP-FedDRC）。该框架通过抑制特征冗余相关性并保留关键判别信息，缓解噪声和数据异质性共同导致的维度坍塌问题。在隐私保护方面，DP-FedDRC在本地训练中采用DP-SGD机制实现梯度裁剪与高斯噪声注入，并基于Rényi差分隐私框架严格核算全局隐私预算。在MNIST、Fashion-MNIST和CIFAR-10数据集上的实验表明，在同等严格的隐私约束下，DP-FedDRC相比基线算法实现了更高的模型准确率，且计算开销仅增加8.3%~9.9%。

Abstract

Federated learning faces dual challenges of data heterogeneity and privacy leakage in distributed privacy-preserving scenarios. Personalized federated learning mitigates data distribution discrepancies by constraining local model update directions

while differential privacy provides rigorous privacy guarantees. However

when solving these two problems at the same time

the existing methods fail to fully consider the coupling effect of noise on the federated learning and training process in the data Non-IID scenario

resulting in insufficient privacy-utility trade-offs. Experiments show that differential privacy noise significantly exacerbates the dimension collapse problem induced by data heterogeneity. This problem leads to the decay of effective degrees of freedom in the feature space and impairs discriminative feature representation

which is a key factor in model performance degradation. To address this

a differential privacy personalized federated learning framework incorporating feature decorrelation and residual constraints (DP-FedDRC) was proposed. This framework alleviates the dimension collapse problem caused by both noise and data heterogeneity by suppressing redundant feature correlations and preserving critical discriminative information. In terms of privacy protection

DP-FedDRC implements gradient clipping and Gaussian noise injection using the DP-SGD mechanism during local training

and rigorously accounts for the global privacy budget based on the Rényi differential privacy framework. Experiments on the MNIST

Fashion-MNIST

and CIFAR-10 datasets demonstrated that under equally strict privacy constraints

DP-FedDRC achieved higher model accuracy compared to baseline algorithms

with only an 8.3%~9.9% increase in computational overhead.

关键词

Keywords

references

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