Data in modern industrial applications and data science present multidimensional progressively, the dimension and the structural complexity of these data are becoming extremely high, which renders existing data analysis methods and machine learning algorithms inadequate to the extent. In addition, high-dimensional data in actual scenarios often share some common latent components and patterns, it is necessary and significant to analyze such data in an associative manner, rather than treating them independently. Considering the problem of data islands and data privacy that is prevalent in the industry. In this article, we propose the first joint high-order orthogonal iterative (J-HOOI) algorithm for simultaneous tensor decomposition and federated tensor decomposition (FTD) model for feature extraction and dimension reduction of high-dimensional industrial data under the federated learning framework. Moreover, we also develop a secure federated computation process based on the J-HOOI method. Using this method, multiple participants iteratively calculate the local factor matrices and transfer the local information to the parameter server, which aggregates the local information to generate the globally updated factor matrices. Finally, each client generates globally compressed features by projecting local data onto these common potential spaces. We have demonstrated with real-world industrial datasets that our approach is similar to a centralized training model in decomposition accuracy and classification accuracy while respecting privacy.

中文翻译：

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基于联合张量分解的工业物联网特征提取方法


现代工业应用和数据科学中的数据逐渐呈现多维化，这些数据的维度和结构复杂性变得极高，这使得现有的数据分析方法和机器学习算法已经到了力不从心的程度。此外，实际场景中的高维数据往往具有一些共同的潜在成分和模式，以关联的方式分析这些数据而不是单独处理它们是必要且有意义的。考虑到业界普遍存在的数据孤岛和数据隐私问题。在本文中，我们提出了第一个联合高阶正交迭代（J-HOOI）算法，用于联合张量分解和联邦张量分解（FTD）模型，用于联邦学习框架下高维工业数据的特征提取和降维。此外，我们还开发了基于 J-HOOI 方法的安全联合计算过程。使用该方法，多个参与者迭代计算局部因子矩阵并将局部信息传输到参数服务器，参数服务器聚合局部信息以生成全局更新的因子矩阵。最后，每个客户端通过将本地数据投影到这些公共潜在空间上来生成全局压缩特征。我们已经用现实世界的工业数据集证明，我们的方法在分解准确性和分类准确性方面类似于集中式训练模型，同时尊重隐私。