Non-intrusive load monitoring (NILM) decomposes the total load reading into appliance-level load signals. Many deep learning-based methods have been developed to accomplish NILM, and the training of deep neural networks (DNN) requires massive load data containing different types of appliances. For local data owners with inadequate load data but expect to accomplish a promising model performance, the conduction of effective NILM co-modelling is increasingly significant. While during the cooperation of local data owners, data exchange and centralized data storage may increase the risk of power consumer privacy breaches . To eliminate the potential risks, a novel NILM method named Fed-NILM ap-plying Federated Learning (FL) is proposed in this paper. In Fed-NILM, local  parameters instead of  load data are shared among local data owners. The global model is obtained by weighted averaging the parameters. In the experiments, Fed-NILM is validated on two real-world datasets . Besides, a comparison of Fed-NILM with locally-trained NILMs and the centrally-trained one is conductedin both residential and industrial scenarios . The experimental results show that Fed-NILM  outperforms locally-trained NILMs and approximate the centrally-trained NILM which is trained on the entire load dataset without privacy preservation .
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 .
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