FCNCP: A Coupled Nonnegative CANDECOMP/PARAFAC Decomposition Based on
  Federated Learning
Yukai Cai Hang Liu Xiulin Wang Hongjin Li Ziyi Wang Chuanshuai Yang Fengyu Cong
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.