A Privacy-Aware Federated Hybrid Model for Multimodal Mental Health Analysis

As mental health disorders such as stress, anxiety, depression, and post-traumatic stress disorder (PTSD) affect a substantial part of the world population, current diagnostic methodologies are still centralized, subjective, and sensitive to privacy concerns. To mitigate these limitations, this study presents a new framework for multimodal mental health classification within a privacy-preserving federated learning framework learning using electroencephalography (EEG), electrocardiography (ECG) and galvanic skin response (GSR) signals. Furthermore, we pro-pose a hybrid deep learning architecture, which combines CNN-LSTM-Transformer blocks to effectively learn spatial, temporal, and long-range dependencies within physiological signals. After preprocessing the cleaned data through artifact removal, band-pass filtering, normalization and multimodal feature fusion signal quality is improved. The proposed model is trained in a federated setting with multiple clients for decentralized training without sharing raw data allowing it to preserve privacy and communication efficiency supporting non-IID data extensions. We evaluate on two datasets, SAM40 (stress detection) and DAPS (anxiety, depression, and PTSD classification). The proposed framework achieved 97% accuracy on SAM40 and more than 96% accuracy on DAPS. Comparative assessments with recent federated and centralized methods validate its strength in multimodal fusion and robust feature exploitation. These results demonstrate the possibility of a general framework for designing privacy-preserving and efficient mental health monitoring systems that can support both Clinical and Wearable-device applications.