FCNCP: A Coupled Nonnegative CANDECOMP/PARAFAC Decomposition Based on Federated Learning

TL;DR

This study provides new tools and approaches for processing and analyzing high-dimensional EEG data across servers and promotes the advancement of coupled tensor decomposition techniques and their integration with emerging federated learning frameworks, offering significant theoretical and practical value.

Abstract

In the field of brain science, data sharing across servers is becoming increasingly challenging due to issues such as industry competition, privacy security, and administrative procedure policies and regulations. Therefore, there is an urgent need to develop new methods for data analysis and processing that enable scientific collaboration without data sharing. In view of this, this study proposes to study and develop a series of efficient non-negative coupled tensor decomposition algorithm frameworks based on federated learning called FCNCP for the EEG data arranged on different servers. It combining the good discriminative performance of tensor decomposition in high-dimensional data representation and decomposition, the advantages of coupled tensor decomposition in cross-sample tensor data analysis, and the features of federated learning for joint modelling in distributed servers. The algorithm utilises federation learning to establish coupling constraints for data distributed across different servers. In the experiments, firstly, simulation experiments are carried out using simulated data, and stable and consistent decomposition results are obtained, which verify the effectiveness of the proposed algorithms in this study. Then the FCNCP algorithm was utilised to decompose the fifth-order event-related potential (ERP) tensor data collected by applying proprioceptive stimuli on the left and right hands. It was found that contralateral stimulation induced more symmetrical components in the activation areas of the left and right hemispheres. The conclusions drawn are consistent with the interpretations of related studies in cognitive neuroscience, demonstrating that the method can efficiently process higher-order EEG data and that some key hidden information can be preserved.

Figures (6)

• Figure 1: Schematic diagram of the coupled tensor decomposition framework based on federated learning.
• Figure 2: Illustration of the simulation experiment. (a) Tensor data for client 1. (b) Tensor data for client 2. (c) Results after FCNCP decomposition for client1. (d) Result after FCNCP decomposition for client2.
• Figure 3: PCA was used to select the number of components with 95 percent explained cumulative variance.
• Figure 4: Correlation analysis of frequency factor, time factor and channel factor extracted by four-dimensional tensor decomposition for each block of data.
• Figure 5: Components of beta-band oscillations. (a) Activity appeared in the frontal lobe within 75 ms at 15 - 20 Hz. (b) activity occurs in the temporal lobe at 15 - 20 Hz, in the region of 75 ms. (c) Activity occurs in the frontal lobe at 25 - 30 Hz, in the region of 60 ms.