Classical Quantum Federated Learning for Mental Stress Detection

The growing prevalence of mental stress has created a strong need for intelligent systems capable of early and accurate detection. This work proposes a hybrid learning framework that combines classical machine learning, quantum computing techniques, and federated learning to classify stress levels in in-dividuals. Initially, physiological signalsfrom the WESAD dataset are processed to remove noise and normalize feature values. A Random Forest model is then applied to identify the most influential features, which helps reduce data dimensionality and makes the model suitable for quantum processing. The selected features are transformed into quantum states using rotation-based angle encoding with RY gates. A variational quantum neural network is designed to learn patterns within the encoded data through parameterized quantum circuits. To ensure data privacy and enable decentralized training, a federated learning strategy is incorporated, where multiple clients train models locally and share only model parameters. These parameters are combined using a federated averaging method to form a global model without exposing sensitive data. The developed system categorizes mental stress into three levels: low, medium, and high. The results indicate that integrating quantum learning with federated approaches can effectively handle sensitive health data while maintaining reliable classification performance. This study demonstrates the potential of combining emerging computational paradigms to build secure and efficient healthcare prediction systems.