Breaking Data Silos in Parkinson’s Disease Diagnosis: An Adaptive Federated Learning Approach for Privacy-Preserving Facial Expression Analysis

Abstract

The early diagnosis of Parkinson’s disease (PD) is crucial for potential patients to receive timely treatment and prevent disease progression. Recent studies have shown that PD is closely linked to impairments in facial muscle control, resulting in characteristic “masked face” symptoms. This discovery offers a novel perspective for PD diagnosis by leveraging facial expression recognition and analysis techniques to capture and quantify these features, thereby distinguishing between PD patients and non-PD individuals based on their facial expressions. However, concerns about data privacy and legal restrictions have led to significant “data silos”, posing challenges to data sharing and limiting the accuracy and generalization of existing diagnostic models due to small, localized datasets. To address this issue, we propose an innovative adaptive federated learning approach that aims to jointly analyze facial expression data from multiple medical institutions while preserving data privacy. Our proposed approach comprehensively evaluates each client's contributions in terms of gradient, data, and learning efficiency, overcoming the non-IID issues caused by varying data sizes or heterogeneity across clients. To demonstrate the real-world impact of our approach, we collected a new facial expression dataset of PD patients in collaboration with a hospital. Extensive experiments validate the effectiveness of our proposed method for PD diagnosis and facial expression recognition, offering a promising avenue for rapid, non-invasive initial screening and advancing healthcare intelligence.