Fed-NILM: A Federated Learning-based Non-Intrusive Load Monitoring Method for Privacy-Protection

TL;DR

Fed-NILM introduces a federated learning approach for non-intrusive load monitoring that enhances privacy protection, scalability, and convergence, while maintaining high accuracy comparable to centralized models.

Contribution

This paper presents Fed-NILM, a novel federated learning-based NILM method that enables collaborative model training without sharing load data, addressing privacy concerns and improving scalability.

Findings

Fed-NILM outperforms local NILMs in accuracy.

Fed-NILM approaches the performance of centralized NILM.

Fed-NILM demonstrates superior scalability and convergence.

Abstract

Non-intrusive load monitoring (NILM) is essential for understanding customer's power consumption patterns and may find wide applications like carbon emission reduction and energy conservation. The training of NILM models requires massive load data containing different types of appliances. However, inadequate load data and the risk of power consumer privacy breaches may be encountered by local data owners during the NILM model training. To prevent such potential risks, a novel NILM method named Fed-NILM which is based on Federated Learning (FL) is proposed in this paper. In Fed-NILM, local model parameters instead of local load data are shared among multiple data owners. The global model is obtained by weighted averaging the parameters. Experiments based on two measured load datasets are conducted to explore the generalization ability of Fed-NILM. Besides, a comparison of Fed-NILM with locally-trained NILMs and the centrally-trained NILM is conducted. The experimental results show that Fed-NILM has superior performance in scalability and convergence. Fed-NILM outperforms locally-trained NILMs operated by local data owners and approximates the centrally-trained NILM which is trained on the entire load dataset without privacy protection. The proposed Fed-NILM significantly improves the co-modeling capabilities of local data owners while protecting power consumers' privacy.