Federated Learning based on Self-Evolving Gaussian Clustering

TL;DR

This paper introduces a federated learning approach utilizing a self-evolving Gaussian clustering method that dynamically adapts its clusters, enhancing decentralized data processing without predefining cluster numbers.

Contribution

It presents a novel federated learning framework with an evolving fuzzy system that automatically adjusts clusters, improving clustering and classification performance on standard datasets.

Findings

Outperforms traditional classification methods on UCI datasets

Demonstrates effective decentralized data processing

Shows significant advantages despite computational complexity

Abstract

In this study, we present an Evolving Fuzzy System within the context of Federated Learning, which adapts dynamically with the addition of new clusters and therefore does not require the number of clusters to be selected apriori. Unlike traditional methods, Federated Learning allows models to be trained locally on clients' devices, sharing only the model parameters with a central server instead of the data. Our method, implemented using PyTorch, was tested on clustering and classification tasks. The results show that our approach outperforms established classification methods on several well-known UCI datasets. While computationally intensive due to overlap condition calculations, the proposed method demonstrates significant advantages in decentralized data processing.