Decentralized, Explainable, and Personalized Mental Health Monitoring

Amidst the evolving landscape of mental health care, the imperative for non-intrusive, continuous data collection is juxtaposed with the concern for patient privacy and the inherent challenges of maintaining engagement. In this pro-posed work, we aim to address privacy challenges in mental health by combining decentralized, federated learning, privacy- preservation techniques, and explanation generation for per-sonalized insights using data from various sources, including smartphones and personal health devices.