Federated Deep Learning for Solving an Image Classification Problem on a Desktop Grid System
摘要
Abstract
The paper considers the adaptation of federated deep learning on a desktop grid system using the example of an image classification problem. Restrictions are imposed on data transfer between the nodes of the desktop grid only for a part of the dataset. The implementation of federated deep learning on a desktop grid system based on the BOINC platform is considered. Methods for generating local datasets for desktop grid nodes are discussed. The results of numerical experiments are presented.