FedMindPurpose: A Federated AI Framework for Purpose Discovery and Mental Health Support in Youth

Scalable, adaptable, and privacy-protecting digital solutions are needed to tackle rising teenage anxiety, depression, and identity theft. Data heterogeneity, non-IID distributions, and ethical concerns around sensitive data aggregation hinder typical centralized AI systems' mental health analytics capabilities. Due to these constraints, this study proposes FedMindPurpose, a Federated Learning Framework for Youth Purpose Discovery and Mental Health (FedMindPurpose), which enables decentralized institutional nodes to participate. This proposed framework handles non-uniform distributions via federated averaging and adaptive optimization. Transformer-based encoders capture semantic information from introspective writings, generative neural networks chronicle social support and interaction patterns, and temporal attention modules imitate mood changes. Federated meta-learning customises models by maintaining global consistency while correcting demographic and cultural variability. With high privacy guarantees (ε=1.2 under differential privacy), a cross-institutional dataset of 1 2, 4 0 0 adolescent records demonstrated a 22.4 % decrease in early stress detection inaccuracy and a 17.6 % increase in purpose-discovery accuracy. The results show that FedMindPurpose provides comprehensive, moral, and adaptive AI-powered assistance for adolescents to develop a sense of purpose and mental resilience.