DP$^2$-NILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring
This work addresses the problem of balancing utility and privacy in NILM for smart grid applications, but it is incremental as it builds on existing federated learning methods.
The paper tackles the lack of comprehensive research on utility optimization and privacy-preserving schemes in federated learning-based non-intrusive load monitoring (NILM) by developing a distributed and privacy-preserving framework (DP2-NILM) and testing it on real-world datasets, achieving evaluation through extensive comparative experiments.
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 consumption, can help analyze electricity consumption behaviours of users and enable practical smart energy and smart grid applications. Recent studies have proposed many novel NILM frameworks based on federated deep learning (FL). However, there lacks comprehensive research exploring the utility optimization schemes and the privacy-preserving schemes in different FL-based NILM application scenarios. In this paper, we make the first attempt to conduct FL-based NILM focusing on both the utility optimization and the privacy-preserving by developing a distributed and privacy-preserving NILM (DP2-NILM) framework and carrying out comparative experiments on practical NILM scenarios based on real-world smart meter datasets. Specifically, two alternative federated learning strategies are examined in the utility optimization schemes, i.e., the FedAvg and the FedProx. Moreover, different levels of privacy guarantees, i.e., the local differential privacy federated learning and the global differential privacy federated learning are provided in the DP2-NILM. Extensive comparison experiments are conducted on three real-world datasets to evaluate the proposed framework.