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FLAME: Federated Learning and Aggregated Multi-Model Ensemble for Multi-Class Alzheimer's Disease Stage

Karim Gasmi¹, Lassaad Ben Ammar², Moez Krichen^3,4

¹Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.

Diagnostics (Basel, Switzerland)

|July 15, 2026

Summary

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This study introduces FLAME, a federated learning system for classifying Alzheimer's disease (AD) stages. The hybrid ensemble model achieved high accuracy, improving early diagnosis and treatment strategies for AD.

Area of Science:

Artificial Intelligence
Computational Neuroscience
Medical Informatics

Background:

Accurate Alzheimer's disease (AD) staging is vital for timely intervention and treatment.
Clinical classification is challenging due to overlapping cognitive profiles across disease stages.
Existing methods lack privacy-preserving, multi-institutional capabilities for robust AD staging.

Purpose of the Study:

To develop and evaluate FLAME, an advanced diagnostic system using federated learning for privacy-preserving AD stage classification.
To systematically review machine learning (ML) and deep learning (DL) models for classifying five AD stages: cognitively normal (CN), subjective memory complaints (SMC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD.
To optimize a hybrid ensemble model using a Genetic Algorithm (GA) for enhanced classification performance.

Keywords:

Alzheimer’s disease classification SDG 3 clinical tabular data deep neural network hybrid ensemble learning multi-class classification privacy-preserving machine learning weight optimisation

Related Experiment Videos

Main Methods:

Evaluated 16 ML and 11 DL models on a 362-feature clinical dataset.
Developed a GA-optimized hybrid ensemble combining LightGBM and a 5-layer DNN.
Implemented a federated learning architecture with four aggregation algorithms (FedAvg, FedProx, FedNova, SCAFFOLD) for privacy-preserving multi-institutional deployment.

Main Results:

FT-Transformer achieved the highest standalone DL performance (accuracy=0.7810, κ=0.7081).
LightGBM showed superior ML performance (accuracy=0.8156, κ=0.7537).
The GA-optimized hybrid ensemble (w=0.685) outperformed individual models. FedNova hybrid design achieved the best federated performance (accuracy=0.8213, κ=0.7614).

Conclusions:

Federated learning combined with evolutionary ensemble optimization offers a scalable, privacy-preserving solution for AD stage classification.
FLAME provides a clinically viable framework for multi-institutional decision-support systems.
Severe under-recall of the AD class (F1 ≤ 0.21) remains the primary challenge for clinical translation.