collaborators

6 papers

cs.AI2025

An Intelligent Fault Self-Healing Mechanism for Cloud AI Systems via Integration of Large Language Models and Deep Reinforcement Learning

Ze Yang, Yihong Jin, Juntian Liu +1

As the scale and complexity of cloud-based AI systems continue to increase, the detection and adaptive recovery of system faults have become the core challenges to ensure service r…

cs.LG2025

Anomaly Detection and Early Warning Mechanism for Intelligent Monitoring Systems in Multi-Cloud Environments Based on LLM

Yihong Jin, Ze Yang, Juntian Liu +1

With the rapid development of multi-cloud environments, it is increasingly important to ensure the security and reliability of intelligent monitoring systems. In this paper, we pro…

cs.NI2025

Research on Cloud Platform Network Traffic Monitoring and Anomaly Detection System based on Large Language Models

Ze Yang, Yihong Jin, Juntian Liu +3

The rapidly evolving cloud platforms and the escalating complexity of network traffic demand proper network traffic monitoring and anomaly detection to ensure network security and…

cs.DC2025

Adaptive Fault Tolerance Mechanisms of Large Language Models in Cloud Computing Environments

Yihong Jin, Ze Yang, Xinhe Xu +2

With the rapid evolution of Large Language Models (LLMs) and their large-scale experimentation in cloud-computing spaces, the challenge of guaranteeing their security and efficienc…

cs.CR2025

Research on Large Language Model Cross-Cloud Privacy Protection and Collaborative Training based on Federated Learning

Ze Yang, Yihong Jin, Yihan Zhang +2

The fast development of large language models (LLMs) and popularization of cloud computing have led to increasing concerns on privacy safeguarding and data security of cross-cloud…

cs.CL2025

HADES: Hardware Accelerated Decoding for Efficient Speculation in Large Language Models

Ze Yang, Yihong Jin, Xinhe Xu

Large Language Models (LLMs) have revolutionized natural language processing by understanding and generating human-like text. However, the increasing demand for more sophisticated…