FeDeFo: A Personalized Federated Deep Forest Framework for Alzheimer’s Disease Diagnosis

Abstract

Alzheimer’s disease (AD) is a neurodegenerative disease that severely affects cognition, memory, and behavior and is incurable. Mild cognitive impairment (MCI) is a clinical precursor to AD, and early diagnosis of AD is essential for the prevention and intervention of disease progression. The hippocampus is one of the first brain regions affected by AD, and therefore structural magnetic resonance images (sMRI) are commonly used to measure the shape and volume of the hippocampus. In this paper, we propose a federal deep forest model called FeDeFo for calculating hippocampal volume using sMRI images to achieve AD classification. Firstly, to effectively protect data privacy, we use a federated learning framework to collaboratively train a gradient boosting decision tree (GBDT) model based on the local data of each client. In addition, to address the data discrepancy between clients, we introduce a deep forest model to exploit the local data beyond local interactions further and fuse it with the federally trained GBDT to personalize the model for each client. The experiments demonstrate that our proposed approach is able to personalize the model while protecting the data privacy of each client, providing a new idea for AD classification.