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

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.