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Non-intrusive load monitoring (NILM), which usually utilizes machine learningmethods and is effective in disaggregating smart meter readings from thehousehold-level into appliance-level consumptions, can help to analyzeelectricity consumption behaviours of users and enable practical smart energyand smart grid applications. However, smart meters are privately owned anddistributed, which make real-world applications of NILM challenging. To thisend, this paper develops a distributed and privacy-preserving federated deeplearning framework for NILM (FederatedNILM), which combines federated learningwith a state-of-the-art deep learning architecture to conduct NILM for theclassification of typical states of household appliances. Through extensivecomparative experiments, the effectiveness of the proposed FederatedNILMframework is demonstrated.

FederatedNILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring based on Federated Deep Learning