Verfahren und vorrichtung zum gemeinsamen trainieren eines logistischen regressionsmodells

Abstract

Some embodiments of this specification provide methods for jointly training a logistic regression model. The training includes three types of training data: a sample characteristic, a sample label, and a model parameter, each of which is split into fragments that are distributed between two parties. The method is performed by either first party of the two parties, and includes: performing masking on three first-party fragments corresponding to the three types of training data by respectively using first fragments of three random numbers in a first fragment of a random array to obtain three first mask fragments, and sending the three first mask fragments to a second party, where the first fragment of the random array is a fragment, sent by a third party to the first party, of two-party fragments that are obtained by splitting values in the random array generated by the third party; constructing three pieces of mask data corresponding to the three types of training data by using the three first mask fragments and three second mask fragments received from the second party; and performing a first calculation based on the three pieces of mask data and the first fragment of the random array to obtain a first gradient fragment for updating the first-party fragment of the model parameter.