BP Learning Method with Simplified Secure Distributed Processing Using Decomposed Data

Abstract

A BP learning method has been proposed for secure distributed processing using decomposed data. Although this method is a secure and safe learning method, it has the drawbacks of a large number of parameters and a large number of communications between servers. To improve these drawbacks, we propose a learning method in which the decomposed parameters are updated only at the central server. We show the number of parameters and communication cost of the conventional and proposed methods, and evaluate their accuracy through numerical experiments. As a result, we show that the model with Q servers can achieve an accuracy comparable to the conventional model with 1/Q decomposition parameters.