Non-intrusive load monitoring (NILM) has gained widespread attention for improving residential energy efficiency by analyzing appliance-level energy consumption. Although machine learning-based NILM methods have demonstrated excellent performance

their effectiveness heavily relies on the availability of sufficient training data. Federated learning (FL) has emerged as a promising approach for collaboratively training NILM models by aggregating distributed knowledge across clients

effectively harnessing decentralized data reserves. However

due to significant data distribution discrepancies among clients

the global model aggregated through FL often deviates from the client-specific optimum. To address this challenge

this paper proposes a directed knowledge transfer-based personalized model learning method. In this method

clients acquire eligible models through peer-to-peer communication and perform cross-architecture knowledge transfer via knowledge distillation. Furthermore

a data-driven model trust evaluation mechanism is designed to pre-screen candidate models and guide directed knowledge transfer

thereby reducing communication overhead and improving transfer efficiency. Additionally

consistency learning is introduced to mitigate potential overfitting during personalized model training. Extensive experiments on three public datasets

PLAID

WHITED and HOUIDI

demonstrate that the proposed method achieves superior training efficiency and performance compared to existing methods.