FedAR+: A Federated Learning Approach to Appliance Recognition with Mislabeled Data in Residential Buildings
This addresses privacy and data quality issues for energy conservation in smart homes, though it is incremental as it builds on existing federated learning methods.
The paper tackles appliance recognition in residential buildings using federated learning to preserve privacy and handle mislabeled data, achieving effective performance with up to 30% noisy labels and outperforming prior solutions on accuracy.
With the enhancement of people's living standards and rapid growth of communication technologies, residential environments are becoming smart and well-connected, increasing overall energy consumption substantially. As household appliances are the primary energy consumers, their recognition becomes crucial to avoid unattended usage, thereby conserving energy and making smart environments more sustainable. An appliance recognition model is traditionally trained at a central server (service provider) by collecting electricity consumption data, recorded via smart plugs, from the clients (consumers), causing a privacy breach. Besides that, the data are susceptible to noisy labels that may appear when an appliance gets connected to a non-designated smart plug. While addressing these issues jointly, we propose a novel federated learning approach to appliance recognition, called FedAR+, enabling decentralized model training across clients in a privacy preserving way even with mislabeled training data. FedAR+ introduces an adaptive noise handling method, essentially a joint loss function incorporating weights and label distribution, to empower the appliance recognition model against noisy labels. By deploying smart plugs in an apartment complex, we collect a labeled dataset that, along with two existing datasets, are utilized to evaluate the performance of FedAR+. Experimental results show that our approach can effectively handle up to $30\%$ concentration of noisy labels while outperforming the prior solutions by a large margin on accuracy.