Lightweight Federated Learning for On-Device Non-Intrusive Load Monitoring

Abstract

Non-intrusive load monitoring (NILM) is a critical technology for disaggregating appliance-specific energy usage by only observing household-level power consumption. If NILM can be performed on end devices (such as smart meters), it can facilitate electricity demand identification and electricity behavior perception for real-time demand-side energy management. However, implementing high-performance NILM models on end devices presents an unresolved issue, encompassing two primary challenges: hardware resource constraints and data resource paucity on end devices. To this end, this paper proposes a lightweight federated learning approach for on-device NILM by combining neural architecture search (NAS) and federated learning. Firstly, a memory-efficient NAS approach is investigated to determine a personalized model within the resource constraints of end devices. Secondly, a federated mutual learning approach is designed to orchestrate the cooperation of distributed end devices with heterogeneous personalized models in a privacy-preserving manner. Case studies on two real-world datasets verify that the proposed method for appliance-level power disaggregation outperforms conventional methods in accuracy and efficiency.