Constraint-Driven Warm-Freeze for Efficient Transfer Learning in Photovoltaic Systems

• URL: http://arxiv.org/abs/2604.05807v1
• Date: Tue, 07 Apr 2026 12:44:19 GMT
• Title: Constraint-Driven Warm-Freeze for Efficient Transfer Learning in Photovoltaic Systems
• Abstract summary: Deep learning models are robust to bias, drift, and transient spikes, yet lightweight enough for resource-constrained edge controllers.<n>We propose Constraint-Driven Warm-Freeze (CDWF), a budget-aware adaptation framework.<n>We show CDWF retains 90 to 99% of full fine-tuning performance while reducing trainable parameters by up to 120x.
• Score: 39.83783914171527
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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