Uncertainty-aware federated temporal learning with explainable LLM-based coaching for privacy-preserving wearable health systems
Abstract
Introduction: The growing use of wearable sensors enables continuous health and activity monitoring; however, challenges such as noisy data, privacy concerns, and device heterogeneity limit the effectiveness of centralized learning systems. This paper presents a privacy-first, AI-enhanced lifestyle coaching framework that integrates multi-stage signal cleaning, federated learning (FL), and explainable large language models (LLMs) with structured prompting for intelligent, decentralized health guidance. Unlike prior FL-based human activity recognition (HAR) systems, this work introduces (i) a quantitatively validated multi-stage denoising pipeline, (ii) local latent-space semantic imputation under federated constraints, and (iii) an explainable LLM-based coaching layer whose reasoning is explicitly grounded in aggregated federated activity representations rather than raw sensor data.
Methods: The proposed system employs a robust preprocessing pipeline comprising Hampel filtering, adaptive wavelet-Butterworth denoising, Variational Autoencoder (VAE)-based semantic imputation, and Kalman smoothing. The cleaned signals are used to train highly efficient Deep LSTM models on-device, and local updates are aggregated using a FedProx-based framework to mitigate client drift and preserve data privacy. To ensure rigorous evaluation and eliminate temporal data leakage, strict chronological data splitting is enforced alongside a 50% overlap stride constraint.
Results: The multi-stage…
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