# Federated Multilinear Principal Component Analysis with Applications in Prognostics

arXiv: https://arxiv.org/abs/2312.06050 · HTML: https://mlsanity.com/paper/2312.06050/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Builds on (most cited first, 8 of 8)

- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Practical Secure Aggregation for Federated Learning on User-Held Data](https://mlsanity.com/paper/1611.04482/) (2016)
- [The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning](https://mlsanity.com/paper/2111.05326/) (2021)
- [A novel approach for fusion of heterogeneous sources of data](https://mlsanity.com/paper/1803.00138/) (2018)
- [Structured Point Cloud Data Analysis via Regularized Tensor Regression for Process Modeling and Optimization](https://mlsanity.com/paper/1807.10278/) (2018)
- [A Supervised Tensor Dimension Reduction-Based Prognostics Model for Applications with Incomplete Imaging Data](https://mlsanity.com/paper/2207.11353/) (2022)
- [Efficient Text Classification Using Tree-structured Multi-linear Principal Component Analysis](https://mlsanity.com/paper/1801.06607/) (2018)
- [Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data](https://mlsanity.com/paper/2309.03439/) (2023)

## Built on by (most cited first, 3 of 3)

- [Deep Learning-Based Residual Useful Lifetime Prediction for Assets with Uncertain Failure Modes](https://mlsanity.com/paper/2405.06068/) (2024)
- [Differentially Private Log-Location-Scale Regression Using Functional Mechanism](https://mlsanity.com/paper/2404.08715/) (2024)
- [Heterogeneity-Aware Personalized Federated Learning for Industrial Predictive Analytics](https://mlsanity.com/paper/2604.19451/) (2026)

## 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.