基于相關(guān)性的Swarm聯(lián)邦降維方法

李文平; 杜選

doi:10.16383/j.aas.c220690

基于相關(guān)性的Swarm聯(lián)邦降維方法

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

李文平^1,,
杜選^1,

1.
嘉興學(xué)院信息科學(xué)與工程學(xué)院嘉興 314001

基金項目: 教育部人文社會(huì )科學(xué)研究規劃基金 (23YJAZH068), 嘉興市科技特派員專(zhuān)項項目(K2022A015)資助

詳細信息

作者簡(jiǎn)介:
李文平：嘉興學(xué)院信息科學(xué)與工程學(xué)院副教授. 主要研究方向為隱私保護技術(shù). 本文通信作者. E-mail: liwenping@hrbeu.edu.cn

杜選：嘉興學(xué)院信息科學(xué)與工程學(xué)院副教授. 主要研究方向為隱私保護技術(shù). E-mail: duxuan@zjxu.edu.cn

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- 文章訪(fǎng)問(wèn)數: 308
- HTML全文瀏覽量: 190
- PDF下載量: 56
- 被引次數: 0
出版歷程
- 收稿日期: 2022-09-01
- 錄用日期: 2023-04-12
- 網(wǎng)絡(luò )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: 聯(lián)邦學(xué)習(Federated learning, FL)在解決人工智能(Artificial intelligence, AI)面臨的隱私泄露和數據孤島問(wèn)題方面具有顯著(zhù)優(yōu)勢. 針對聯(lián)邦學(xué)習的已有研究未考慮聯(lián)邦數據之間的關(guān)聯(lián)性和高維性問(wèn)題, 提出一種基于聯(lián)邦數據相關(guān)性的去中心化聯(lián)邦降維方法. 該方法基于Swarm學(xué)習(Swarm learning, SL)思想, 通過(guò)分離耦合特征, 構建典型相關(guān)分析(Canonical correlation analysis, CCA)的Swarm聯(lián)邦框架, 以提取Swarm節點(diǎn)的低維關(guān)聯(lián)特征. 為保護協(xié)作參數的隱私安全, 還構建一種隨機擾亂策略來(lái)隱藏Swarm特征隱私. 在真實(shí)數據集上的實(shí)驗驗證了所提方法的有效性.
- 隱私保護 /
- Swarm學(xué)習 /
- 聯(lián)邦學(xué)習 /
- 典型相關(guān)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

圖 1 SCCA協(xié)作序列

Fig. 1 Collaboration sequences of the SCCA

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圖 2 來(lái)自IMDB-WIKI的圖像示例

Fig. 2 The sample images selected from IMDB-WIKI

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圖 3 主向量對分類(lèi)精度的影響

Fig. 3 Influence of the principal vectors on classification accuracy

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圖 4 主向量對數據量的影響

Fig. 4 Influence of the principal vectors on data size

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圖 5 性別識別精度

Fig. 5 Recognition accuracy for gender

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圖 6 訓練時(shí)間比較

Fig. 6 Comparison of training time

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圖 7 降維方法比較

Fig. 7 Comparison of dimension reduction methods

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圖 8 分類(lèi)實(shí)例

Fig. 8 An instance of classification

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