Early diagnosis of Parkinson's disease (PD) is crucial for timely treatment and disease management. Recent studies link PD to impaired facial muscle control, manifesting as "masked face" symptoms, offering a novel diagnostic approach through facial expression analysis. However, data privacy concerns and legal restrictions have resulted in significant "data silos", hindering data sharing and limiting the accuracy and generalizability of existing diagnostic models due to small, localized datasets. To address these challenges, we propose an innovative Tri-Factor Adaptive Federated Learning (TriAFL) framework, designed to collaboratively analyze facial expression data across multiple medical institutions while ensuring robust data privacy protection. TriAFL introduces a comprehensive evaluation mechanism that assesses client contributions across three dimensions: gradient, data, and learning efficiency, effectively addressing Non-IID issues arising from data size variations and heterogeneity. To validate the real-world applicability of our method, we collaborate with a hospital to build the largest known facial expression dataset of PD patients. Furthermore, we explore the integration of local data augmentation strategy to further enhance diagnostic accuracy. Comprehensive experimental results demonstrate TriAFL's superior performance over conventional FL methods in classification task, as well as confirms TriAFL's efficacy in PD diagnosis, delivering a rapid, non-invasive screening tool while driving advancements in AI-powered healthcare.

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.