Federated learning for cognitive impairment detection using speech data.

ABSTRACT:

Introduction

In Alzheimer's disease (AD) research, clinical, neuroimaging, genetic, and biomarker data are vital for advancing its understanding and treatment. However, privacy concerns and limited datasets complicate data sharing. Federated learning (FL) offers a solution by enabling collaborative research while preserving data privacy.

Methods

This study analyzed data from patients assessed at the Memory Unit of the Ace Alzheimer Center Barcelona who completed a standardized digital speech protocol. Acoustic features extracted from these recordings were used to distinguish between cognitively unimpaired (CU) and cognitively impaired (CI) individuals. The aim was to evaluate how data heterogeneity impacted the FL model performance across three scenarios: (1) equal contribut

SUBMITTER: Blazquez-Folch J 

PROVIDER: S-EPMC12548476 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature