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              基于相關性的Swarm聯邦降維方法

              李文平 杜選

              李文平, 杜選. 基于相關性的Swarm聯邦降維方法. 自動化學報, xxxx, xx(x): x?xx doi: 10.16383/j.aas.c220690
              引用本文: 李文平, 杜選. 基于相關性的Swarm聯邦降維方法. 自動化學報, xxxx, xx(x): x?xx doi: 10.16383/j.aas.c220690
              Li Wen-Ping, Du Xuan. Swarm federated dimensionality reduction method based on correlation. Acta Automatica Sinica, xxxx, xx(x): x?xx doi: 10.16383/j.aas.c220690
              Citation: Li Wen-Ping, Du Xuan. Swarm federated dimensionality reduction method based on correlation. Acta Automatica Sinica, xxxx, xx(x): x?xx doi: 10.16383/j.aas.c220690

              基于相關性的Swarm聯邦降維方法

              doi: 10.16383/j.aas.c220690
              基金項目: 嘉興市科技特派員專項項目(K2022A015)資助
              詳細信息
                作者簡介:

                李文平:嘉興學院信息科學與工程學院副教授. 主要研究方向為隱私保護技術. 本文通訊作者. E-mail: liwenping@hrbeu.edu.cn

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

              Swarm Federated Dimensionality Reduction Method Based on Correlation

              Funds: Supported by 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: 聯邦學習(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題方面具有顯著優勢. 針對聯邦學習的已有研究未考慮聯邦數據之間的關聯性和高維性問題, 提出一種基于聯邦數據相關性的去中心化聯邦降維方法. 該方法基于Swarm學習(Swarm learning, SL)思想, 通過分離耦合特征, 構建典型相關分析(Canonical correlation analysis, CCA)的Swarm聯邦框架, 以提取Swarm節點的低維關聯特征. 為保護協作參數的隱私安全, 還構建了一種隨機擾亂策略來隱藏Swarm特征隱私. 在真實數據集上的實驗驗證了所提方法的有效性.
              • 圖  1  SCCA協作序列

                Fig.  1  Collaboration sequences of the SCCA

                圖  2  來自IMDB-WIKI的圖像示例

                Fig.  2  The sample images selected from IMDB-WIKI

                圖  3  主向量對分類精度的影響

                Fig.  3  Influence of the principal vectors on classification accuracy

                圖  4  主向量對數據量的影響

                Fig.  4  Influence of the principal vectors on data size

                圖  5  性別識別精度

                Fig.  5  Recognition accuracy for gender

                圖  6  訓練時間比較

                Fig.  6  Comparison of training time

                圖  7  降維方法比較

                Fig.  7  Comparison of dimension reduction methods

                圖  8  分類實例

                Fig.  8  An instance of classification

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                        • 收稿日期:  2022-09-01
                        • 錄用日期:  2023-04-12
                        • 網絡出版日期:  2023-10-18

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