SmartFRZ is an efficient training framework that uses attention-based layer freezing to reduce training costs and accelerate model training. The framework automatically determines which layers to freeze during training based on the attention mechanism, which helps maintain model accuracy while reducing computational resources. The core of SmartFRZ is a lightweight attention-based predictor that analyzes historical weight data to decide when to freeze layers. This predictor is trained using a layer representational similarity-based method, allowing it to generalize across different models and datasets. SmartFRZ outperforms existing layer freezing methods in terms of training efficiency and accuracy. It significantly reduces training time and computation costs, achieving higher accuracy than prior methods in both fine-tuning and training from scratch scenarios. The framework also reduces memory costs by minimizing intermediate data during back-propagation. SmartFRZ is effective across various domains, including computer vision and natural language processing, and demonstrates robustness to different attention window sizes and layer sizes. The results show that SmartFRZ provides significant improvements in training efficiency and accuracy, making it a promising solution for efficient model training.SmartFRZ is an efficient training framework that uses attention-based layer freezing to reduce training costs and accelerate model training. The framework automatically determines which layers to freeze during training based on the attention mechanism, which helps maintain model accuracy while reducing computational resources. The core of SmartFRZ is a lightweight attention-based predictor that analyzes historical weight data to decide when to freeze layers. This predictor is trained using a layer representational similarity-based method, allowing it to generalize across different models and datasets. SmartFRZ outperforms existing layer freezing methods in terms of training efficiency and accuracy. It significantly reduces training time and computation costs, achieving higher accuracy than prior methods in both fine-tuning and training from scratch scenarios. The framework also reduces memory costs by minimizing intermediate data during back-propagation. SmartFRZ is effective across various domains, including computer vision and natural language processing, and demonstrates robustness to different attention window sizes and layer sizes. The results show that SmartFRZ provides significant improvements in training efficiency and accuracy, making it a promising solution for efficient model training.