With the advancement of the large language model (LLM), the demand for data labeling services has increased dramatically. Big models are inseparable from high-quality, specialized scene data, from training to deploying application iterations to landing generation. However, how to achieve intelligent labeling consistency and accuracy and improve labeling efficiency in distributed data middleware scenarios is the main difficulty in enhancing the quality of labeled data at present. In this paper, we proposed an asynchronous federated learning optimization method based on the combination of LLM and digital twin technology. By analysising and comparing and with other existing asynchronous federated learning algorithms, the experimental results show that our proposed method outperforms other algorithms in terms of performance, such as model accuracy and running time. The experimental validation results show that our proposed method has good performance compared with other algorithms in the process of intelligent labeling both in terms of accuracy and running solves the consistency and accuracy problems of intelligent labeling in a distributed data center.

With the continuous improvement of the performance of artificial intelligence and neural networks, a new type of computing architecture-edge computing, came into being. However, when the scale of hybrid intelligent edge systems expands, there are redundant communications between the node and the parameter server; the cost of these redundant communications cannot be ignored. This paper proposes a reputation-based asynchronous model update scheme and formulates the federated learning scheme as an optimization problem. First, the explainable reputation consensus mechanism for hybrid intelligent labeling systems communication is proposed. Then, during the process of local intelligent data annotation, significant challenges in consistency, personalization, and privacy protection posed by the federated recommendation system prompted the development of a novel federated recommendation framework utilizing a graph neural network. Additionally, the method of information interaction model fusion was adopted to address data heterogeneity and enhance the uniformity of distributed intelligent annotation. Furthermore, to mitigate communication delays and overhead, an asynchronous federated learning mechanism was devised based on the proposed reputation consensus mechanism. This mechanism leverages deep reinforcement learning to optimize the selection of participating nodes, aiming to maximize system utility and streamline data sharing efficiency. Lastly, integrating the learned models into blockchain technology and conducting validation ensures the reliability and security of shared data. Numerical findings underscore that the proposed federated learning scheme achieves higher learning accuracy and enhances communication efficiency.

The chain-of-thought (CoT) approach in large language models (LLMs) has markedly enhanced their performance on complex tasks; however, effectively distilling this capability into LLMs with smaller parameter scales remains a challenge. Studies have found that small LLMs do not always benefit from CoT distillation. Inspired by the concept of teaching students in accordance with their aptitude, we propose an adaptive chain-of-thought distillation (ACoTD) framework. The core idea is to dynamically and adaptively customize distillation data and supervision signals for student models based on their performance on the original problems. Specifically, ACoTD initially evaluates and categorizes the original problems according to the capabilities of the student model. Subsequently, for Easy- and Medium-level problems, a short CoT distillation is employed for a brief lecture to reinforce knowledge and enhance training efficiency, for high-difficulty problems where the student model underperforms, and a detailed long CoT distillation is utilized for in-depth explanation to infuse richer reasoning logic. This differentiated distillation strategy ensures that student models achieve a better grasp of learning. We conducted experiments on multiple benchmark datasets. The results indicate that, compared to the baseline, our method can significantly improve the inference performance of small LLMs. Our method provides a new student-centered paradigm for knowledge distillation, demonstrating that adaptive adjustment of teaching strategies based on student feedback is an effective way to enhance small LLMs’ reasoning ability.