Federated learning-based non-intrusive load monitoring adaptive to real-world heterogeneities
Year: 2025
Type: article
Abstract: Non-intrusive load monitoring (NILM) is a key way to cost-effectively acquire appliance-level information in advanced metering infrastructure (AMI). Recently, federated learning has enabled NILM to learn from decentralized meter data while preserving privacy. However, as real-world heterogeneities in electricity consumption data, local models, and AMI facilities cannot be eliminated in advance, federated learning-based NILM (FL-NILM) may underperform or even fail. Therefore, we propose a FL-NILM method adaptive to these heterogeneities. To fully leverage diverse electricity consumption data, dynamic clustering is integrated into cloud aggregation to hierarchically mitigate the global-local bias in knowledge required for NILM. Meanwhile, adaptive model initialization is applied in local training to balance biased global knowledge with local accumulated knowledge, enhancing the learning of heterogeneous data. To further handle heterogeneous local NILM models, homogeneous proxy models are used for global-local iteration through knowledge distillation. In addition, a weighted aggregation mechanism with a cache pool is designed for adapting to asynchronous iteration caused by heterogeneous AMI facilities. Experiments on public datasets show that the proposed method outperforms existing methods in both synchronous and asynchronous settings. The proposed method's advantages in computing and communication complexity are also discussed.
Source: Scientific Reports
Authors: Qingquan Luo, Chaofan Lan, Tao Yu, Minhang Liang, Wencong Xiao, Zhenning Pan
Institution: South China University of Technology
Language: English
Topic: Smart Grid Energy Management
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Awards: G1374462839, G2167525414, G3207261313
OpenAlex ID: W4410717890