Fed-NILM: A Federated Learning-based Non-Intrusive Load Monitoring Method for Privacy-Protection
This addresses privacy concerns for power consumers and data owners in energy monitoring, though it is incremental as it applies existing federated learning to a specific domain.
The paper tackles the problem of training non-intrusive load monitoring (NILM) models while protecting user privacy by proposing Fed-NILM, a federated learning-based method that shares model parameters instead of raw data, and it shows superior scalability and convergence, approximating the performance of a centrally-trained model without privacy protection.
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.