PFEDSOLAR: PERSONALIZED FEDERATED LEARNING FOR PHOTOVOLTAIC PANEL SOILING RECOGNITION

The intelligent operation and maintenance (O&M) of photovoltaic (PV) power plants is crucial for ensuring their power generation efficiency and economic benefits, among which the automatic recognition of PV panel soiling types is one of the core tasks. However, subject to variations in illumination, climate, and pollution sources, PV power plants across different geographical locations exhibit significant non-independent and identically distributed (non-IID) characteristics in their soiling images. Furthermore, commercial confidentiality and location privacy concerns restrict the aggregation of individual power plant data into large-scale centralized datasets, resulting in widespread “data silos.” To tackle these challenges, we propose pFedSolar, a personalized federated learning (PFL) approach tailored for PV panel soiling recognition, which leverages the vision-language foundation model CLIP. This method incorporates three core components: a federated domain adversarial learning module, a decoupled text embedding mechanism, and a personalized federated aggregation and optimization strategy. Extensive experiments on a composite dataset demonstrate that pFedSolar significantly outperforms state-of-the-art (SOTA) methods, ablation studies also confirm the efficacy of each core component.