Federated Learning Model with Augmentation and Samples Exchange Mechanism

The use of intelligent solutions often comes down to the use of already trained classifiers, which is caused by one of their biggest drawbacks. It is the accuracy or effectiveness of artificial intelligence methods, which are algorithms called data-hungry. It means that it depends on the number of samples in the database, and the quality of the classifier could be better if their number is high and the samples are different. In this paper, we propose a solution based on the idea of federated learning in an application for intelligent systems. The proposed solution consists not only in the division of the database among workers but also in the quality of the samples and their possible exchange. Exchanging samples for a particular worker means labeling difficult to classify samples. These samples are used to expand the sets using the generative adversarial network. The mathematical model of a proposal is described, then the experimental results are shown and discussed with the comparison to the classic approach.