Privacy-Preserving and Explainable Federated Edge Learning for Multimodal Wearable-Based Self-Tracking and Monitoring

Abstract

The rapid growth of multimodal wearable devices has enabled continuous monitoring of physiological and behavioral patterns for home-based health applications. However, centralized data processing raises serious privacy concerns and limits real-time, interpretable insights. This study proposes PEX-FEL, a privacy-preserving and explainable federated edge learning framework for stress detection and activity recognition in decentralized environments. The framework combines federated learning with differential privacy (ε ≤ 1.0) and secure aggregation to protect user data. Low-Rank Adaptation (LoRA) is applied for efficient local training on resource-constrained edge devices, while SHAP is used to provide interpretable, user-centric explanations of predictions. Experiments were conducted using WESAD, PPG-DaLiA, and SWELL datasets under non-IID conditions to simulate real-world heterogeneity. Results show that a hybrid CNN-LSTM model achieved 0.85 accuracy, 0.85 F1-score, and 0.90 AUC-ROC, outperforming centralized approaches by 8–10%. The framework also maintained strong privacy (membership inference < 0.52) and low latency (~45 ms). SHAP analysis identified heart rate variability and electrodermal activity as key stress indicators. Overall, this work demonstrates a balanced approach to accuracy, privacy, efficiency, and interpretability in wearable health monitoring systems.