Proximal Federated Learning for Body Mass Index Monitoring using Commodity WiFi

Body Mass Index (BMI) is a critical metric for assessing public health and identifying populations at risk for obesity-related conditions. Traditional BMI monitoring methods often raise privacy concerns and require active cooperation from individuals, limiting their applicability in real-world scenarios. This paper introduces a novel approach to BMI monitoring that leverages proximal federated learning (PFL) using commodity WiFi devices. Our method addresses the challenges of data heterogeneity and intermittent connectivity in FL. By our approach, the Adaptive Elastic Stochastic Alternating Direction Method of Multipliers (AESADMM), an optimization algorithm designed to handle data heterogeneity and intermittent connectivity in FL scenarios, our system collects Channel State Information (CSI) from WiFi signals to passively classify BMI based on the impact of different body shapes on signal propagation. This approach ensures privacy preservation and eliminates the need for active participant involvement. Theoretical analysis and empirical results demonstrate the superior accuracy, reduced communication costs, and enhanced scalability of our proposed method compared to existing personalized FL frameworks, showcasing its potential as an effective tool for large-scale BMI monitoring in diverse environments.