Personalized differential privacy federated learning method for collaborative spectrum sensing

Tang Xiangyun; Kang Jiawen; Han Xu; Zhang Tao; Liu Yinqiu; Sun Geng; Jiao Yutao

doi:10.11959/j.issn.1000-436x.2026067

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Personalized differential privacy federated learning method for collaborative spectrum sensing

Papers | 更新时间：2026-05-07

- Personalized differential privacy federated learning method for collaborative spectrum sensing
- Journal on Communications Vol. 47, Issue 4, Pages: 97-112(2026)
- 作者机构：
  
  1.中央民族大学信息工程学院，北京 100081
  2.广东工业大学自动化学院，广东广州 510006
  3.北京交通大学网络空间安全学院，北京 100091
  4.新加坡南洋理工大学，新加坡 639798
  5.吉林大学计算机科学与技术学院，吉林长春 130012
  6.中国人民解放军陆军工程大学通信工程学院，江苏南京 210007
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62572132;62572502;62571548);National Cryptography Foundation (NSFC)(2025NCSF02030);NSFC Youth Science Fund Project (Category C)(62302539;62402029);Beijing Natural Science Foundation Fengtai Joint Fund(L251041)
- DOI：10.11959/j.issn.1000-436x.2026067
  CLC： TN92
- Received：01 December 2025，
  
  Revised：2026-03-04，
  
  Accepted：11 March 2026，
  
  Published：20 April 2026
- 稿件说明：
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唐湘云,康嘉文,韩旭等.面向协作频谱感知的个性化差分隐私联邦学习方法[J].通信学报,2026,47(04):97-112.

Tang Xiangyun,Kang Jiawen,Han Xu,et al.Personalized differential privacy federated learning method for collaborative spectrum sensing[J].Journal on Communications,2026,47(04):97-112.
唐湘云,康嘉文,韩旭等.面向协作频谱感知的个性化差分隐私联邦学习方法[J].通信学报,2026,47(04):97-112. DOI： 10.11959/j.issn.1000-436x.2026067.

Tang Xiangyun,Kang Jiawen,Han Xu,et al.Personalized differential privacy federated learning method for collaborative spectrum sensing[J].Journal on Communications,2026,47(04):97-112. DOI： 10.11959/j.issn.1000-436x.2026067.

摘要

针对协作频谱感知中数据非独立同分布（Non-IID）特性导致的模型性能下降问题，提出了一种融合个性化差分隐私与重平衡分簇策略的联邦学习方案（RebalFL）。该方案首先引入个性化差分隐私机制，允许数据设置差异化隐私预算，在保障隐私的同时减少噪声注入；其次，设计重平衡分簇策略，构建数据分布均衡的客户端簇，缓解模型漂移问题。实验结果表明，RebalFL在Non-IID场景下显著优于现有差分隐私方法，能有效提升频谱感知模型在隐私保护下的分类精度与鲁棒性。

Abstract

To address the degradation of model performance caused by data non-independent and identically distributed (Non-IID) characteristics in collaborative spectrum sensing

a federated learning scheme RebalFL was proposed

which integrated personalized differential privacy with a rebalancing clustering strategy. First

a personalized differential privacy mechanism that allowed heterogeneous privacy budgets for different data sources was introduced

thereby reducing noise injection while preserving privacy. Then

a rebalancing clustering strategy was designed to form client clusters with more balanced data distributions and mitigate model drift. Experimental results show that RebalFL outperforms existing differential privacy methods in Non-IID scenarios

substantially improving the classification accuracy and robustness of spectrum sensing models under privacy protection.

关键词

Keywords

references

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