## 5 Discussion  
To further explore the impact of fine-tuning on model performance, our experiment analyzes the influence of the number of fine-tuning samples on model performance. Starting from 0 or 1, the number of fine-tuning samples is doubled in each iteration until all  
Figure 8: The impact of the number of iterations on model F1_score._  
Figure 9: The impact of the number of iterations on model error rate.  
samples are used. After each fine-tuning iteration, TPs (true positives) and TNs (true negatives) are recorded for the subsequent calculation of performance metrics. A curve graph depicting the model's performance evaluation based on the number of samples includes four indicators: accuracy, precision, recall, and F1-score. An analysis of these four metrics reveals that after fine-tuning with 1100 samples, the model's performance stabilizes and reaches its optimal state, as shown in Figure 10.  
In the experiments on the number of fine-tuning samples, it was observed that model performance improved as the number of samples increased. This suggests that more training data can help the model generalize better, thereby enhancing its performance on the validation set. However, once the number of samples reaches a certain threshold, the performance improvement stabilizes. This may be because the model has become sufficiently complex to capture all relevant patterns in the data, or due to the emergence of overfitting.  
In assessing model performance, this paper pays special attention to metrics such as accuracy, precision, recall, F1-score, and false positive rate. An increase in accuracy indicates an enhanced ability of the model to correctly identify sensitive data. An improvement in recall suggests that the model performs better at not missing sensitive data. The F1-score, as the harmonic mean of accuracy and recall, provides a comprehensive measure of performance. A reduction in the false positive rate signifies progress in reducing the misidentification of non-sensitive data by the model.  
Through the experimental performance analysis in this paper, it can be concluded that the proposed federated learning approach combined with large models such as Llama3,  
Figure 10: Analysis of the impact of fine-tuning sample size. (**a**) The impact of the sample number on accuracy. (**b**) The impact of the sample number on precision. (**c**) The impact of the sample number on F1-Score. (**d**) The impact of the sample number on Recall.  
Llama3.1, Qwen 2.5, and the LoRA instruction fine-tuning technique is effective in improving model performance. The model demonstrates good performance improvements with increasing training epochs and fine-tuning sample sizes. Meanwhile, a comparative analysis of the performance of the five large models selected in this chapter reveals that the Qwen2.5 14B model can achieve convergence with fewer iterations. This means that under the same training conditions, the Qwen2.5 14B model can approximate the optimal solution faster, achieving similar or better performance levels compared to the Llama3.1 model with fewer training iterations. This rapid convergence characteristic makes the Qwen2.5 14B model more efficient in practical applications, saving significant training time and computational resources. Especially in scenarios requiring rapid response and iteration, this advantage of the Qwen2.5 14B model is particularly prominent. Furthermore, the model's performance on key performance indicators also demonstrates its potential in practical applications. Future work will focus on further optimizing model performance and exploring more efficient federated learning strategies.