A federated framework for speech-based early detection of Alzheimer’s disease

Mohamed Mourad Abdellattif, Abdelrahman Mohamed Farouk, Nada Hamada Ahmed, Nadine Ahmed Elquersh, Ahmed Hamdy Elshennawy, Noha S. Tawfik

Abstract


The development of artificial intelligence for Alzheimer’s disease (AD) diagnostics is often hindered by data privacy regulations that prevent the aggregation of sensitive patient information. Federated Learning (FL) offers a decentralized solution, enabling collaborative model training without sharing raw data. This paper presents a robust FL framework for the early detection of AD using spontaneous speech from the ADReSS dataset. We systematically evaluate the optimal components for a privacy-preserving pipeline by simulating a cross-silo federated environment. Our methodology involves comparing multiple feature extraction techniques, where VGGish audio embeddings proved most effective, and two classification models, with the Multi-Layer Perceptron (MLP) demonstrating superior performance. We further optimized the framework by comparing FedAvg, FedAvgM, and FedProx aggregation strategies, identifying FedAvgM as the most stable and effective. Our results show that the collaborative FL model significantly outperforms models trained on isolated local data. The final optimized framework achieved a state-of-the-art accuracy of 87.50% and 81.25% in a 2-client and 3-client setting, respectively. This study validates the feasibility of using federated learning to build scalable, accurate, and ethical diagnostic tools for Alzheimer’s disease.

 

Received on, 09 August 2025

Accepted on, 25 August  2025 

Published on, 11 November 2025


Keywords


Machine learning; Federated Learning;

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DOI: https://dx.doi.org/10.21622/ACE.2025.05.2.1567

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Copyright (c) 2025 Mohamed Mourad Abdellattif, Abdelrahman Mohamed Farouk, Nada Hamada Ahmed, Nadine Ahmed Elquersh, Ahmed Hamdy Elshennawy, Noha Seddik

Advances in Computing and Engineering

E-ISSN: 2735-5985

P-ISSN: 2735-5977

 

Published by:

Academy Publishing Center (APC)

Arab Academy for Science, Technology and Maritime Transport (AASTMT)

Alexandria, Egypt

ace@aast.edu