Dynamic Clustering in Federated Learning

TL;DR

This paper introduces a novel three-phase clustering algorithm using GANs for federated learning in wireless networks, enhancing privacy and adaptability, and significantly improving handover prediction accuracy.

Contribution

It presents a new GAN-based clustering method with calibration and division phases to address privacy, fixed cluster shapes, and non-adaptive cluster numbers in federated learning.

Findings

The proposed algorithm preserves data privacy during clustering.

It adapts to dynamic environments by modifying clusters.

It improves handover prediction accuracy by 43%.

Abstract

In the resource management of wireless networks, Federated Learning has been used to predict handovers. However, non-independent and identically distributed data degrade the accuracy performance of such predictions. To overcome the problem, Federated Learning can leverage data clustering algorithms and build a machine learning model for each cluster. However, traditional data clustering algorithms, when applied to the handover prediction, exhibit three main limitations: the risk of data privacy breach, the fixed shape of clusters, and the non-adaptive number of clusters. To overcome these limitations, in this paper, we propose a three-phased data clustering algorithm, namely: generative adversarial network-based clustering, cluster calibration, and cluster division. We show that the generative adversarial network-based clustering preserves privacy. The cluster calibration deals with dynamic environments by modifying clusters. Moreover, the divisive clustering explores the different number of clusters by repeatedly selecting and dividing a cluster into multiple clusters. A baseline algorithm and our algorithm are tested on a time series forecasting task. We show that our algorithm improves the performance of forecasting models, including cellular network handover, by 43%.