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

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.