Federated Learning-Based Anomaly Detection for Environment Monitoring Sensor Networks

This letter proposes a lightweight distributed time series anomaly detection method for environment monitoring sensor networks. To overcome environmental disturbance and ensure computation efficiency, for each sensor node, the observed data are converted into symbol sequences, and then trained through a self-learning model to obtain the local detection threshold. Within the framework of federated learning (FL), the global threshold is calculated using an energy-weighted method based on the local thresholds, and then used to continuously update the detection thresholds of sensor nodes. After training, any of sensor nodes independently determines whether an anomaly occurs by its own detection threshold. The proposed method has been successfully applied to indoor environment monitoring and experimental results demonstrate that our method has obvious advantages in terms of detection accuracy and energy efficiency.