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Multilinear Principal Component Analysis (MPCA) is a widely utilized methodfor the dimension reduction of tensor data. However, the integration of MPCAinto federated learning remains unexplored in existing research. To tackle thisgap, this article proposes a Federated Multilinear Principal Component Analysis(FMPCA) method, which enables multiple users to collaboratively reduce thedimension of their tensor data while keeping each user's data local andconfidential. The proposed FMPCA method is guaranteed to have the sameperformance as traditional MPCA. An application of the proposed FMPCA inindustrial prognostics is also demonstrated. Simulated data and a real-worlddata set are used to validate the performance of the proposed method.

Federated Multilinear Principal Component Analysis with Applications in Prognostics