What question did this study set out to answer?

The research aims to develop a framework for early diagnosis of Alzheimer's disease while preserving patient privacy.

May 29, 2026Open Access

Self-Aware Quantization for Privacy-Preserving Federated Learning: Enhancing Alzheimer Diagnosis with an Adaptive Inception-Transformer Framework

Key Points

The research aims to develop a framework for early diagnosis of Alzheimer's disease while preserving patient privacy.
Utilized Self-Aware Quantization Adaptive Federated Transfer Learning framework with FedProx and Inception-Transformer Networks.
Achieved computational efficiency with a 45% reduction in model size and a 35% decrease in communication overhead.
Employed advanced aggregation methods to stabilize training on non-IID datasets.
Achieved 98.0% accuracy, 97.6% precision, 98.15% recall, and 98.0% F1-score on the OASIS dataset.
Demonstrated improved generalization for clients with limited data, enhancing the framework's effectiveness.

Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative disease that requires early diagnosis to effectively treat and slow down the progression of the disease. To deal with all these issues, we come up with the Self-Aware Quantization Adaptive Federated Transfer Learning framework with FedProx-Optimized Inception-Transformer Networks (SAQ-AFTL-ITN) that is capable of finding accurate AD diagnosis, without hindering the privacy of patients' data. The SAQ-AFTL-ITN framework achieves phenomenal computational efficiency by slashing model size by 45% and communication overhead by 35%, breaking the bottleneck for decentralized healthcare environments. By using FedProx, the stabilization of the training on non-independent and identically distributed (non-IID) datasets is achieved, while TL helps to improve the generalization power of the model, particularly for clients having very limited data. Without noticing, this research shows that this framework, by combining Inception modules with Transformer encoders and using advanced aggregation methods, has already attained state-of-the-art results on the OASIS dataset, attaining an accuracy of 98.0%, precision of 97.6%, recall of 98.15%, and an F1-score of 98.0%. This work unveils the game-changing capabilities of FL integrated with SAQ, thereby forming new benchmarks for systems for medical AI that are privacy-preserving, scalable, and efficient.

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