Work

FWM‐CL: Federated Watermark With Closed‐Loop Architecture for Efficient Non‐Intrusive Load Monitoring in Smart Grids

Year: 2026

Type: article

Abstract: ABSTRACT With the increasing deployment of smart grids, non‐intrusive load monitoring (NILM) has emerged as a crucial technology for estimating power consumption of individual devices using only aggregated measurements from the main power meter. Although federated learning (FL) offers a privacy‐preserving solution for NILM by enabling collaborative model training across distributed smart meters, current FL‐based NILM methods face two critical challenges: vulnerability to Byzantine attacks and inefficient local power disaggregation. This paper introduces the federated watermark closed‐loop learning (FWM‐CL) method to address these issues. Using inherent model redundancy, FWM‐CL employs stable parameters as watermarks to detect and counter Byzantine attacks, significantly improving the robustness of the system. Furthermore, it integrates a feedback mechanism to optimise local power disaggregation network, achieving high accuracy with reduced computational complexity. Extensive evaluations on the UK‐DALE and REDD datasets demonstrate that FWM‐CL outperforms state‐of‐the‐art methods in both Byzantine attack resistance and power disaggregation performance.

Source: IET Generation Transmission & Distribution

DOI: 10.1049/gtd2.70200

Authors: Yuefeng Lu, Shijin Xu, Yadong Liu, Xiuchen Jiang

Institutions: Shanghai Jiao Tong University, China Southern Power Grid (China)

Language: English

Topic: Smart Grid Energy Management

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,940