CDWF achieves 90-99% of full fine-tuning performance with up to 120x fewer trainable parameters by dynamically allocating full trainability to gradient-important blocks and LoRA to others for PV cyberattack transfer learning.
Smart energy guardian: A hybrid deep learning model for detecting fraudulent pv generation,
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
A federated learning framework fuses solar irradiance and PV generation data via co-attention, uses prototype alignment for imbalance, and aggregates models across communities to detect generation fraud while preserving privacy.
citing papers explorer
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Constraint-Driven Warm-Freeze for Efficient Transfer Learning in Photovoltaic Systems
CDWF achieves 90-99% of full fine-tuning performance with up to 120x fewer trainable parameters by dynamically allocating full trainability to gradient-important blocks and LoRA to others for PV cyberattack transfer learning.
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Privacy-Preserving Generation Fraud Detection for Distributed Photovoltaic Systems: A Solar Irradiance-Fused Federated Learning Framework
A federated learning framework fuses solar irradiance and PV generation data via co-attention, uses prototype alignment for imbalance, and aggregates models across communities to detect generation fraud while preserving privacy.