Federated Learning for Predicting Mild Cognitive Impairment to Dementia Conversion

Abstract-- Dementia is a progressive condition that impairs an individual's cognitive health and daily functioning, with mild cognitive impairment (MCI) often serving as its precursor. The prediction of MCI-to-dementia conversion has been well studied, but previous studies have almost always focused on traditional Machine Learning (ML)--based methods that require sharing sensitive clinical information to train predictive models. This work highlights that FL can eliminate the need for data sharing without compromising model efficacy. An early dementia diagnosis is essential for guiding appropriate management strategies and implementing timely I. Predicting loss of the structure and functions of the neurons, resulting whether an individual suffering from MCI will have a in a diverse group of disorders such as Alzheimer's disease, dementia diagnosis in future has been considered to be a Parkinson's disease and others. Neurodegenerative diseases key aspect towards early dementia diagnosis and large-scale cause a decrease in cognitive functions, affecting memory studies on this MCI-to-dementia conversion prediction are and/or behavioral abilities, finally interfering with the quality clearly warranted.