Recent advancements in technology, particularly in artificial intelligence and privacy-preserving tools, have facilitated the development of more sophisticated and secure approaches to addressing this challenge. This paper introduces FedWell, a novel privacy-preserving Federated occupant Stress monitoring approach for smart building environments. Our system integrates physiological data from smart yoga pillows (SaYoPillow) and wearable environmental sensors to train a lightweight, edge-deployable Artificial Neural Network (ANN) model. The FedWell framework ensures data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. We conducted experiments using a comprehensive dataset of physiological parameters for stress level detection. The results demonstrate the global model’s exceptional performance, achieving 99.95% accuracy in stress level recognition, with precision of 99.95%, recall of 99.93%, and an F1-score of 99.94%. The global model exhibits a minimal loss of 0.0019% and a low communication cost of 0.08 Mb, highlighting its efficiency for real-time applications.