Alzheimer’s Disease (AD), the prevailing form of dementia, is a neurological condition that significantly impacts individuals globally, leading to devastating effects. The early detection of AD is of paramount importance in mitigating its impact. Numerous methodologies have been suggested for diagnosing AD through medical imaging techniques such as positron emission tomography (PET) and magnetic resonance imaging (MRI). Nevertheless, it is anticipated that utilizing blood biomarkers would enhance the identification of individuals with AD and cognitive impairments. This paper introduces an innovative distributed deep-learning methodology for the early identification of AD through the analysis of blood samples. This study aims to investigate the application of federated learning (FL) in the analysis of blood samples to predict the likelihood of getting AD. Our study employed a dataset of many blood …