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

by   Haijin Wang, et al.

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.


page 1

page 2

page 3

page 4


A Federated Learning Framework for Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) aims at decomposing the total readi...

Privacy-preserving household load forecasting based on non-intrusive load monitoring: A federated deep learning approach

Load forecasting is very essential in the analysis and grid planning of ...

Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learning

In this paper we propose Fed-ensemble: a simple approach that bringsmode...

A Federated F-score Based Ensemble Model for Automatic Rule Extraction

In this manuscript, we propose a federated F-score based ensemble tree m...

Fed-FSNet: Mitigating Non-I.I.D. Federated Learning via Fuzzy Synthesizing Network

Federated learning (FL) has emerged as a promising privacy-preserving di...

Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data

Generative Adversarial Networks (GANs) are typically trained to synthesi...

Personalized Subgraph Federated Learning

In real-world scenarios, subgraphs of a larger global graph may be distr...

Please sign up or login with your details

Forgot password? Click here to reset