Federated Multilinear Principal Component Analysis with Applications in Prognostics
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Abstract (arXiv)
Multilinear Principal Component Analysis (MPCA) is a widely utilized method for the dimension reduction of tensor data. However, the integration of MPCA into federated learning remains unexplored in existing research. To tackle this gap, this article proposes a Federated Multilinear Principal Component Analysis (FMPCA) method, which enables multiple users to collaboratively reduce the dimension of their tensor data while keeping each user's data local and confidential. The proposed FMPCA method is guaranteed to have the same performance as traditional MPCA. An application of the proposed FMPCA in industrial prognostics is also demonstrated. Simulated data and a real-world data set are used to validate the performance of the proposed method.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 24 references that are in the map. First posted on arXiv 11 Dec 2023.
Builds on
8 of 24 references.
References · 8
- 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
- 2016Practical Secure Aggregation for Federated Learning on User-Held Datacited by 178
- 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
- 2018A novel approach for fusion of heterogeneous sources of datacited by 13
- 2018Structured Point Cloud Data Analysis via Regularized Tensor Regression for Process Modeling and Optimizationcited by 8
- 2022A Supervised Tensor Dimension Reduction-Based Prognostics Model for Applications with Incomplete Imaging Datacited by 5
- 2018Efficient Text Classification Using Tree-structured Multi-linear Principal Component Analysiscited by 2
- 2023Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Datacited by 2
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Citing papers per year: 2024: 2; 2025: 0; 2026: 1.
3 papers.
- 2024Deep Learning-Based Residual Useful Lifetime Prediction for Assets with Uncertain Failure Modescited by 2
- 2024Differentially Private Log-Location-Scale Regression Using Functional Mechanismcited by 0
- 2026Heterogeneity-Aware Personalized Federated Learning for Industrial Predictive Analyticscited by 0
- 2026Heterogeneity-Aware Personalized Federated Learning for Industrial Predictive Analytics21 Apr 2026
- 2024Deep Learning-Based Residual Useful Lifetime Prediction for Assets with Uncertain Failure Modes9 May 2024
- 2024Differentially Private Log-Location-Scale Regression Using Functional Mechanism12 Apr 2024