基于相关性的Swarm联邦降维方法

李文平; 杜选

doi:10.16383/j.aas.c220690

基于相关性的Swarm联邦降维方法

doi: 10.16383/j.aas.c220690 cstr: 32138.14.j.aas.c220690

李文平^1,,
杜选^1,

1.
嘉兴学院信息科学与工程学院嘉兴 314001

基金项目: 教育部人文社会科学研究规划基金 (23YJAZH068), 嘉兴市科技特派员专项项目(K2022A015)资助

详细信息

作者简介:
李文平：嘉兴学院信息科学与工程学院副教授. 主要研究方向为隐私保护技术. 本文通信作者. E-mail: liwenping@hrbeu.edu.cn

杜选：嘉兴学院信息科学与工程学院副教授. 主要研究方向为隐私保护技术. E-mail: duxuan@zjxu.edu.cn

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- 文章访问数: 313
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- 被引次数: 0
出版历程
- 收稿日期: 2022-09-01
- 录用日期: 2023-04-12
- 网络出版日期: 2023-10-18
- 刊出日期: 2024-09-19

Swarm Federated Dimensionality Reduction Method Based on Correlation

LI Wen-Ping^1
,,
DU Xuan^1
,

1.
College of Information Science and Engineering, Jiaxing University, Jiaxing 314001

Funds: Supported by Humanity and Social Science Planning Foundation of Ministry of Education of China (23YJAZH068) and Jiaxing Science and Technology Commissioner Special Project (K2022A015)

More Information

Author Bio:
LI Wen-Ping　Associate professor at the College of Information Science and Engineering, Jiaxing University. His main research interest is privacy protection. Corresponding author of this paper

DU Xuan　Associate professor at the College of Information Science and Engineering, Jiaxing University. His main research interest is privacy protection

摘要

摘要: 联邦学习(Federated learning, FL)在解决人工智能(Artificial intelligence, AI)面临的隐私泄露和数据孤岛问题方面具有显著优势. 针对联邦学习的已有研究未考虑联邦数据之间的关联性和高维性问题, 提出一种基于联邦数据相关性的去中心化联邦降维方法. 该方法基于Swarm学习(Swarm learning, SL)思想, 通过分离耦合特征, 构建典型相关分析(Canonical correlation analysis, CCA)的Swarm联邦框架, 以提取Swarm节点的低维关联特征. 为保护协作参数的隐私安全, 还构建一种随机扰乱策略来隐藏Swarm特征隐私. 在真实数据集上的实验验证了所提方法的有效性.
- 隐私保护 /
- Swarm学习 /
- 联邦学习 /
- 典型相关分析
Abstract: Federated learning (FL) has significant advantages in solving the problems of privacy disclosure and data islands faced by artificial intelligence (AI). Previous studies on federated learning do not consider the problems of relevance and high dimensionality of data distributed among different federations. Based on the relevance of federated data, a decentralized federated dimensionality reduction method is proposed. This method draws on the idea of Swarm learning (SL). Based on the separation of coupling features, a Swarm federated framework for canonical correlation analysis (CCA) is constructed to extract the low dimensional correlation features of Swarm nodes. In order to protect the privacy of collaboration parameters, a random disturbance strategy is also constructed to hide the privacy of Swarm features. Experiments on real data sets verify the effectiveness of the proposed method.
- Privacy protection /
- Swarm learning (SL) /
- federated learning (FL) /
- canonical correlation analysis (CCA)

HTML全文

图 1 SCCA协作序列

Fig. 1 Collaboration sequences of the SCCA

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图 2 来自IMDB-WIKI的图像示例

Fig. 2 The sample images selected from IMDB-WIKI

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图 3 主向量对分类精度的影响

Fig. 3 Influence of the principal vectors on classification accuracy

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图 4 主向量对数据量的影响

Fig. 4 Influence of the principal vectors on data size

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图 5 性别识别精度

Fig. 5 Recognition accuracy for gender

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图 6 训练时间比较

Fig. 6 Comparison of training time

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图 7 降维方法比较

Fig. 7 Comparison of dimension reduction methods

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图 8 分类实例

Fig. 8 An instance of classification

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