Personalizing Federated Learning based on Adapting Representation Learning for Anomaly Detection in Home Health Monitoring Systems⋆ - Vu, Ly; Nguyen, Trung Tin (VNU Journal of Science: Computer Science and Communication Engineering; Article in Press, 2026)
Abstract: The proliferation of Internet of Things (IoT) wearable devices has enabled real-time human activity recognition for home health monitoring, including fall detection. However, deploying machine learning models in these scenarios faces challenges from data privacy concerns and the nonIndependently and Identically Distributed (non-IID) nature of sensor data across users. Federated learning (FL) addresses privacy by enabling collaborative training without centralized data collection, but existing FL frameworks struggle with data heterogeneity. We propose FL based on Adaptive Local Training (FedALT), where each client’s model consists of an Autoencoder (AE) for learning latent representations and a Predictor (PD) for classifying activities, including abnormal actions such as falls. The key innovation is the Adaptive Local Training (ALT) mechanism, which dynamically adjusts the contribution of global and local AE models during local initialization using only the unsupervised RE loss, thereby mitigating bias caused by non-IID label distributions. Experiments on three wearable sensor datasets, i.e., MobiAct, MobiFall, and HAR, demonstrate that FedALT consistently outperforms existing FL frameworks, particularly in classifying minority and abnormal action classes under heterogeneous data distributions. Keywords: Anomaly Detection, Federated learning, IoT.