Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application

TL;DR

This paper introduces a hierarchical clustering-based federated learning approach to improve depression severity prediction from heart rate variability data, addressing non-IID data challenges in privacy-sensitive healthcare applications.

Contribution

It proposes Personalized Federated Cluster Models that enhance model personalization and accuracy for non-IID medical data in federated learning settings.

Findings

Improved prediction accuracy for depression severity.

Effective handling of non-IID heart rate data.

Potential for broader application in healthcare federated learning.

Abstract

While machine learning techniques are being applied to various fields for their exceptional ability to find complex relations in large datasets, the strengthening of regulations on data ownership and privacy is causing increasing difficulty in its application to medical data. In light of this, Federated Learning has recently been proposed as a solution to train on private data without breach of confidentiality. This conservation of privacy is particularly appealing in the field of healthcare, where patient data is highly confidential. However, many studies have shown that its assumption of Independent and Identically Distributed data is unrealistic for medical data. In this paper, we propose Personalized Federated Cluster Models, a hierarchical clustering-based FL process, to predict Major Depressive Disorder severity from Heart Rate Variability. By allowing clients to receive more personalized model, we address problems caused by non-IID data, showing an accuracy increase in severity prediction. This increase in performance may be sufficient to use Personalized Federated Cluster Models in many existing Federated Learning scenarios.