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Federated learning for cognitive impairment detection using speech data.

Josep Blazquez-Folch1, María Limones Andrade2, Berta Calm1

  • 1Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya, Barcelona, Spain.

Frontiers in Artificial Intelligence
|October 27, 2025
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Summary
This summary is machine-generated.

Federated learning (FL) effectively uses speech data to detect cognitive impairment, even with varied data distributions. This privacy-preserving method enhances collaborative Alzheimer's disease research.

Keywords:
Alzheimer’s diseasecognitive impairmentsdeep learningfederated learningspeech acoustics

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Area of Science:

  • Neuroscience
  • Computer Science
  • Biomedical Engineering

Background:

  • Alzheimer's disease (AD) research requires diverse data, but privacy concerns hinder sharing.
  • Federated learning (FL) offers a privacy-preserving solution for collaborative data analysis.

Purpose of the Study:

  • To evaluate the impact of data heterogeneity on FL model performance for cognitive impairment detection using speech biomarkers.
  • To compare FL performance against local models under various data distribution scenarios.

Main Methods:

  • Acoustic features from digital speech recordings of 2,239 participants (cognitively unimpaired and impaired) were analyzed.
  • Federated Averaging (FedAvg) and Iterative Data Aggregation (IDA) were used to aggregate local models trained with MLP feed-forward neural networks.
  • Model performance was assessed across scenarios with equal/unequal contributions and imbalanced class ratios.

Main Results:

  • FL showed modest accuracy and AUC gains in scenarios with equal contributions and imbalanced classes.
  • In scenarios with unequal contributions, FL significantly improved performance on smaller datasets (balanced accuracy from 0.51 to 0.80) while maintaining high accuracy on larger datasets.
  • FL demonstrated scalability and effectiveness across heterogeneous institutional data.

Conclusions:

  • Federated learning enables collaborative development of speech-based biomarkers for cognitive impairment detection.
  • FL is a scalable, privacy-preserving approach suitable for digital health research in neurodegenerative diseases, addressing data imbalance and institutional disparities.