# Integrating Merkle Trees with Transformer Networks for Secure Financial Computation
## 3 Materials and Method
### Experiment Design  
The design of the experiment is a crucial component in demonstrating the effectiveness of the Merkle-Transformer model. The experiment aims to showcase the advantages of our model in processing financial data and verify its capability to ensure data security. To ensure the accuracy and reliability of the experimental results, a standard dataset division strategy is adopted. Specifically, the dataset is divided into training, validation, and test sets. The training set is used for the learning process of the model, the validation set for adjusting model parameters and hyperparameters, and the test set for the final evaluation of model performance. Typically, the dataset is divided into a 70%, 15%, and 15% ratio, although the actual proportions may vary based on the size and characteristics of the dataset. This division ensures the model's generalization on unseen data and avoids overfitting.  
In the experiments, multiple baseline models were selected for comparison, including Transformer [23], BERT [33], BERT-wwm [34], RoBERTa [35], and SpanBERT [36]. These models have been widely applied in the field of natural language processing and have demonstrated excellent performance across various tasks. Their selection aims to showcase the performance improvement of the Merkle-Transformer model relative to the current state-of-the-art technologies. The Transformer model, as the pioneer of self-attention mechanisms, serves as the foundation for our comparison. The BERT model, known for its powerful contextual understanding capabilities, has established new benchmarks in multiple NLP tasks. BERT-wwm, RoBERTa, and SpanBERT represent further advancements based on BERT, enhancing model performance through different pre-training strategies and architectural optimizations. The choice of these models as baselines is motivated by their representation of the current level of advancement in natural language processing technology and their exceptional ability to handle sequential data. By comparing the Merkle-Transformer model with these baselines, a fair assessment of our model's performance can be conducted, especially in the specific domain of financial data processing.  
The Adam optimizer [37] was chosen for optimization. Adam, an adaptive learning rate optimization algorithm, combines the advantages of momentum and RMSprop optimization methods. It adjusts the learning rate for each parameter based on its update history, making it particularly suitable for handling large-scale and complex datasets. Additionally, the Adam optimizer has been proven effective in various deep learning tasks, thus being the preferred choice. Hyperparameter settings are based on an understanding of the problem and preliminary experimental results, including learning rate, batch size, and the number of training epochs. The learning rate starts high and is gradually reduced during training to refine the adjustment of model weights. The batch size depends on the dataset size, model complexity, and hardware resource limitations. The number of training epochs needs to be sufficient to ensure the model can adequately learn patterns in the data but not so numerous as to cause overfitting.  
Through such an experimental design, a comprehensive evaluation of the Merkle-Transformer model's performance in secure financial computation tasks can be conducted, allowing for a fair comparison with current advanced models. By comparing experimental results, it is expected to demonstrate that the Merkle-Transformer not only provides excellent predictive performance in processing financial data but also enhances the security of the data processing process.