Fair federated learning with privacy protection for heterogeneous data

Wu Ju; Miao Yinbin; Zhang Weibin; Liu Shuying; Ma Jianfeng

doi:10.11959/j.issn.2096-109x.AQ26019

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Fair federated learning with privacy protection for heterogeneous data

Papers | 更新时间：2026-07-15

- Fair federated learning with privacy protection for heterogeneous data
- Chinese Journal of Network and Information Security Vol. 12, Issue 3, Pages: 78-92(2026)
- 作者机构：
  
  1.西安电子科技大学网络与信息安全学院，陕西西安 710126
  2.西北大学计算机学院，陕西西安 710127
  3.咸阳师范学院计算机学院，陕西咸阳 712000
- 作者简介：
- 基金信息：
  
  College Students' Innovation and Entrepreneurship Training Program(S202510722063)
- DOI：10.11959/j.issn.2096-109x.AQ26019
  CLC： TP309
- Received：21 January 2026，
  
  Revised：2026-04-07，
  
  Published：25 June 2026
- 稿件说明：
移动端阅览
吴炬, 苗银宾, 张炜斌, 等. 面向异构数据的具有隐私保护性能的公平联邦学习[J]. 网络与信息安全学报, 2026, 12(3): 78-92.

Wu Ju, Miao Yinbin, Zhang Weibin, et al. Fair federated learning with privacy protection for heterogeneous data[J]. Chinese Journal of Network and Information Security, 2026, 12(3): 78-92.
吴炬, 苗银宾, 张炜斌, 等. 面向异构数据的具有隐私保护性能的公平联邦学习[J]. 网络与信息安全学报, 2026, 12(3): 78-92. DOI： 10.11959/j.issn.2096-109x.AQ26019.

Wu Ju, Miao Yinbin, Zhang Weibin, et al. Fair federated learning with privacy protection for heterogeneous data[J]. Chinese Journal of Network and Information Security, 2026, 12(3): 78-92. DOI： 10.11959/j.issn.2096-109x.AQ26019.

摘要

联邦学习在非独立同分布数据场景下易因梯度冲突导致公平性失衡，且模型参数传输存在隐私泄露风险。现有公平性联邦学习算法虽有效缓解了梯度冲突，但复杂异质数据下局部与全局模型的一致性缺乏针对性优化，导致局部训练偏离全局最优，进而削弱公平性并限制整体模型性能。针对此问题，提出FedALF及LDP-FedALF算法。使用公平驱动目标和分层优化缓解梯度冲突；引入交替方向乘子法进行优化，在客户端局部目标函数中设计对偶变量项与二次惩罚项，构建局部与全局模型一致性约束，进一步提升公平性与模型性能；同时在模型传输中加入自适应本地差分隐私技术保护数据隐私。通过大量实验验证得出，FedALF相较于现有公平联邦学习算法，测试准确率平均提升10.82%，公平性平均提升19.80%；LDP-FedALF相较于现有公平联邦学习算法，测试准确率平均提升7.53%，公平性平均提升15.36%。

Abstract

Federated learning suffers from fairness imbalance due to gradient conflicts in non-independent and indentically distributed (Non-IID) data scenarios

with model parameter transmission risking privacy leakage. Existing fair federated learning algorithms mitigate gradient conflicts effectively but lack targeted optimization for local-global model consistency under complex heterogeneous data

leading to deviated local training from global optima

impaired fairness and limited overall performance. To address this

the FedALF algorithm and LDP-FedALF algorithm were proposed: it alleviates gradient conflicts via fairness-driven objectives and hierarchical optimization; adopts ADMM for optimization

designing dual variables and quadratic penalty terms in clients' local objective functions to enforce local-global model consistency constraints

further enhancing fairness and performance; and integrates adaptive local differential privacy into model transmission for privacy protection. Through a large number of experiments

FedALF achieved an average improvement of 10.82% in test accuracy and 19.80% in fairness performance compared to existing fair federated learning algorithms. LDP-FedALF achieved an average improvement of 7.53% in test accuracy and 15.36% in fairness performance compared to existing fair federated learning algorithms.

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references

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