Integrative Federated Learning Framework for Multimodal Parkinson’s Disease Biomarker Fusion

Accurate and early diagnosis of Parkinson’s Disease (PD) is challenged by the diverse manifestations of motor and non-motor symptoms across different patients. Existing studies largely rely on limited datasets and biomarkers. In this extended research, we propose a comprehensive Federated Learning (FL) framework designed to integrate heterogeneous biomarkers through multimodal combinations—such as EEG–fMRI pairs, continuous speech with vowel pronunciation, and the fusion of EEG, gait, and accelerometry data—drawn from diverse sources and modalities. By processing data separately at client nodes and performing feature and decision fusion at a central server, our method preserves privacy and enables robust PD classification. Experimental results show accuracies exceeding 85% across multiple fusion techniques, with attention-based fusion reaching 97.8% for Freezing of Gait (FoG) detection. Our framework advances scalable, privacy-preserving, multimodal diagnostics for PD.