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

arXiv:2404.11890v1 Announce Type: cross
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 …

abstract analysis arxiv brain competition cs.lg cs.na data data analysis data sharing federated learning industry math.na policies privacy processing regulations science security servers type