# Constraint-Driven Warm-Freeze for Efficient Transfer Learning in Photovoltaic Systems Source: [Constraint-Driven Warm-Freeze for Efficient Transfer Learning in Photovoltaic Systems]() Collection index: [llms.txt]() Data updated: 2026-04-09T05:00:04.534Z Authors: Yasmeen Saeed, Ahmed Sharshar, Mohsen Guizani Published: 2026-04-07T12:44:19.000Z ## Abstract Detecting cyberattacks in photovoltaic (PV) monitoring and MPPT control signals requires models that are robust to bias, drift, and transient spikes, yet lightweight enough for resource-constrained edge controllers. While deep learning outperforms traditional physics-based diagnostics and handcrafted features, standard fine-tuning is computationally prohibitive for edge devices. Furthermore, existing Parameter-Efficient Fine-Tuning (PEFT) methods typically apply uniform adaptation or rely on expensive architectural searches, lacking the flexibility to adhere to strict hardware budgets. To bridge this gap, we propose Constraint-Driven Warm-Freeze (CDWF), a budget-aware adaptation framework. CDWF leverages a brief warm-start phase to quantify gradient-based block importance, then solves a constrained optimization problem to dynamically allocate full trainability to high-impact blocks while efficiently adapting the remaining blocks via Low-Rank Adaptation (LoRA). We evaluate CDWF on standard vision benchmarks (CIFAR-10/100) and a novel PV cyberattack dataset, transferring from bias pretraining to drift and spike detection. The experiments demonstrate that CDWF retains 90 to 99% of full fine-tuning performance while reducing trainable parameters by up to 120x. These results establish CDWF as an effective, importance-guided solution for reliable transfer learning under tight edge constraints. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on CIFAR-10 (Accuracy, Parameter Reduction)](): Image Classification; CIFAR-10; Accuracy: 96.81. - [Cyber-attack Detection on PV MPPT Drift (test)](): Cyber-attack detection; PV MPPT Drift (test); Test Accuracy: 90.1. - [Cyber-attack Detection on PV MPPT Spike (test)](): Cyber-attack detection; PV MPPT Spike (test); Test Accuracy: 94.1. ## Original sources and implementation [Original paper]() [PDF]()