Due to the booming development of computer vision technology and artificial intelligence algorithms, it has become more feasible to implement artificial rearing of animals in real production scenarios. Improving the accuracy of day-age detection of chickens is one of the examples and is of great importance for chicken rearing. This paper focuses on the problem of classifying the age of chickens within 100 days. Due to the huge amount of data and the different computing power of different devices in practical application scenarios, it is important to maximize the computing power of edge computing devices without sacrificing accuracy. This paper proposes a high-precision federated learning-based model that can be applied to edge computing scenarios. In order to accommodate different computing power in different scenarios, this paper proposes a dual-ended adaptive federated learning framework; in order to adapt to low computing power scenarios, this paper performs lightweighting operations on the mainstream model; and in order to verify the effectiveness of the model, this paper conducts a number of targeted experiments. Compared with AlexNet, VGG, ResNet and GoogLeNet, this model improves the classification accuracy to 96.1%, which is 14.4% better than the baseline model and improves the Recall and Precision by 14.8% and 14.2%, respectively. In addition, by lightening the network, our methods reduce the inference latency and transmission latency by 24.4 ms and 10.5 ms, respectively. Finally, this model is deployed in a real-world application and an application is developed based on the wechat SDK.

With the support of cloud computing technology, it is easier for financial institutions to obtain more key information about the whole industry chain. However, the massive use of financial data has many potential risks. In order to better cope with this dilemma and better protect the financial privacy of users, we propose a privacy protection model based on cloud computing. The model provides four levels of privacy protection according to the actual needs of users. At the highest level of protection, the server could not access any information about the user and the raw data, nor could it recover the computational characteristics of the data. In addition, due to the universality of the mathematical principle of linear operators, the model could effectively protect and accelerate all models based on linear operations. The final results showed that the method can increase the speed by 10 times, compared with the privacy protection method that only uses local computing power instead of the cloud server. It can also effectively prevent the user’s privacy from being leaked with relatively minimal delay cost, compared with no privacy protection method. Finally, we design a multi-user scheduling model to deploy the model in a real scenario, which could maximise server power and protect user privacy as well.

In this paper, the Merkle-Transformer model is introduced as an innovative approach designed for financial data processing, which combines the data integrity verification mechanism of Merkle trees with the data processing capabilities of the Transformer model. A series of experiments on key tasks, such as financial behavior detection and stock price prediction, were conducted to validate the effectiveness of the model. The results demonstrate that the Merkle-Transformer significantly outperforms existing deep learning models (such as RoBERTa and BERT) across performance metrics, including precision, recall, accuracy, and F1 score. In particular, in the task of stock price prediction, the performance is notable, with nearly all evaluation metrics scoring above 0.9. Moreover, the performance of the model across various hardware platforms, as well as the security performance of the proposed method, were investigated. The Merkle-Transformer exhibits exceptional performance and robust data security even in resource-constrained environments across diverse hardware configurations. This research offers a new perspective, underscoring the importance of considering data security in financial data processing and confirming the superiority of integrating data verification mechanisms in deep learning models for handling financial data. The core contribution of this work is the first proposition and empirical demonstration of a financial data analysis model that fuses data integrity verification with efficient data processing, providing a novel solution for the fintech domain. It is believed that the widespread adoption and application of the Merkle-Transformer model will greatly advance innovation in the financial industry and lay a solid foundation for future research on secure financial data processing.