Central node and method therein for enabling an aggregated machine learning model from local machine learnings models in a wireless communications network

Abstract

A method for enabling a machine learning model to be aggregated from local machine learning models comprised in at least two local nodes in a wireless communications network is provided. The method comprises receiving, from each of the at least two local nodes, a parametrized function of a local machine learning model, a generator function of a local generative model, and a discriminator function of a local discriminative model, wherein the generator function and the discriminator function are trained on the same data as the parametrized function. The method also comprises determining, for each pair of the at least two local nodes, a first cross-discrimination values by applying the received discriminator function from a first local node of the pair on samples generated using the received generator function from the second local node of the pair, and a second cross-discrimination value by applying the received discriminator function from the second local node of the pair on samples generated using the received generator function from the first local node of the pair. The method further comprises obtaining an aggregated machine learning model based on the determined first and second cross-discrimination values. Furthermore, the method comprises transmitting information indicating the obtained aggregated machine learning model to one or more of the at least two local nodes in the wireless communications network.