PFDRL: Personalized Federated Deep Reinforcement Learning for Residential Energy Management

Abstract

The rise of the Internet of Things (IoT) has increased standby energy consumption due to the growing number of smart devices in homes. Existing approaches use real-time energy data and machine learning to identify and minimize standby energy for residential energy management but rely on cloud-based data aggregation and collaborative training due to limited edge device data. However, such an approach incurs extra cloud service costs, risks personal data leakage, and fails to capture residence diversity, resulting in suboptimal energy management performance.

Topics & Concepts