A Federated Learning Model of Fuzzy c-Lines for Horizontally Distributed Database

Abstract

Privacy preserving data clustering is a useful method for extractingintrinsic cluster structures from distributed databases keeping personal privacy. In this research, a novel model of performing Fuzzy c-Lines clustering with horizontally distributed data is proposed, where federated learning is achieved by sharing gradient information estimatedin each client. The proposed model is an extension of the Fuzzy c-Means-type federated learning model proposed by Pedrycz to linear clustering with least square criterion.