Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household-level into appliance-level consumptions, can help to analyze electricity consumption behaviours of users and enable practical smart energy and smart grid applications. However, smart meters are privately owned and distributed, which make real-world applications of NILM challenging. To this end, this paper develops a distributed and privacy-preserving federated deep learning framework for NILM (FederatedNILM), which combines federated learning with a state-of-the-art deep learning architecture to conduct NILM for the classification of typical states of household appliances. Through extensive comparative experiments, the effectiveness of the proposed FederatedNILM framework is demonstrated.

翻译：非侵入性负载监测(NILM)通常使用机器学习方法,并有效地将智能计数从家庭一级的智能读数分解为家用电器一级的消费,有助于分析用户的电力消费行为,促成实用的智能能源和智能电网应用,但智能仪是私人拥有和分配的,使NILM的实际应用具有挑战性。为此,本文件为NILM(FedNILM)开发了一个分布式的、保护隐私的深层学习框架,将联邦化学习与最先进的深层学习结构相结合,以进行典型家用电器分类的NILM,通过广泛的比较实验,展示了拟议中的联邦化精密框架的有效性。