A consideration on federated learning with autonomous client models

Abstract

Recently, there has been growing interest in Federated Learning (FL) for learning from distributed data. While FL has advantages such as privacy protection and traffic reduction, it is difficult for existing FL models to preciously characterize the data distribution of each client. In this study, we consider an autonomous federated learning scheme that allows to maintain both client and general models to capture the individual and common data for each client, respectively. This scheme enables to extract features of client's own data. Preliminary experiments on the image benchmark data demonstrate the usefulness of the proposed method in terms of the performance and data feature extraction.