Generative adversarial network employed for decentralized and confidential ai training

Abstract

A computer-implemented method is presented for constructing a trained model for a plurality of edge classifiers in a network having a federated classifier, a generator, and a discriminator. The method includes obtaining edge trained models from the plurality of edge devices, each edge trained model being trained independently with data from private data of each edge, training the generator model and discriminator model by employing the edge trained models and an unlabeled set of data by employing a generative adversarial training procedure, generating data samples by the trained generator model, training the federated classifier with the data samples from the generator model, and deploying the trained model back to the plurality of edge devices.