Federated Multilinear Principal Component Analysis with Applications in Prognostics (2312.06050v2)
Abstract: 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.
- Practical secure aggregation for federated learning on user-held data. arXiv preprint arXiv:1611.04482.
- A review of privacy-preserving federated learning for the internet-of-things. Federated Learning Systems, 21–50.
- Image-based prognostics using penalized tensor regression. Technometrics 61(3), 369–384.
- An adaptive functional regression-based prognostic model for applications with missing data. Reliability Engineering & System Safety 133, 266–274.
- Multiple tensor-on-tensor regression: An approach for modeling processes with heterogeneous sources of data. Technometrics 63(2), 147–159.
- Residual life predictions in the absence of prior degradation knowledge. IEEE Transactions on Reliability 58(1), 106–117.
- Pattern clustering of hysteresis time series with multivalued mapping using tensor decomposition. IEEE Transactions on Systems, Man, and Cybernetics: Systems 48(6), 993–1004.
- Unsupervised classification of 12-lead ecg signals using wavelet tensor decomposition and two-dimensional gaussian spectral clustering. Knowledge-Based Systems 163, 392–403.
- Personalized tucker decomposition: Modeling commonality and peculiarity on tensor data. arXiv preprint arXiv:2309.03439.
- Federated optimization: Distributed machine learning for on-device intelligence. arXiv preprint arXiv:1610.02527.
- The internet of federated things (ioft). IEEE Access 9, 156071–156113.
- Incremental sparse linear regression algorithms for face recognition. In 2018 Tenth international conference on advanced computational intelligence (ICACI), pp. 69–74. IEEE.
- Mpca: Multilinear principal component analysis of tensor objects. IEEE transactions on Neural Networks 19(1), 18–39.
- Time-varying functional regression for predicting remaining lifetime distributions from longitudinal trajectories. Biometrics 61(4), 1064–1075.
- Super resolution for multi-sources image stream data using smooth and sparse tensor completion and its applications in data acquisition of additive manufacturing. Technometrics 64(1), 2–17.
- Federated (log)-location-scale regerssion.
- Efficient text classification using tree-structured multi-linear principal component analysis. In 2018 24th international conference on pattern recognition (ICPR), pp. 585–590. IEEE.
- Multilinear principal component analysis for face recognition with fewer features. Neurocomputing 73(10-12), 1550–1555.
- Parallel matrix factorization for low-rank tensor completion. arXiv preprint arXiv:1312.1254.
- Structured point cloud data analysis via regularized tensor regression for process modeling and optimization. Technometrics 61(3), 385–395.
- Federated learning. Synthesis Lectures on Artificial Intelligence and Machine Learning 13(3), 1–207.
- Tensor-based temporal control for partially observed high-dimensional streaming data. Technometrics (just-accepted), 1–21.
- Image-based feedback control using tensor analysis. Technometrics, 1–10.
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