With the rapid development of the Internet of Things (IoT) and edge computing, the scale and complexity of modern networks have increased significantly, driving the demand for distributed fault diagnosis. Federated learning (FL) effectively addresses the issues of data privacy and dispersion by enabling edge devices to...
Jing-Ting Mei, Yang Yang, Celimuge Wu et al.· IEEE Transactions on Cogniti...· 0 citations
With the rapid development of computer and network technology, network and software logs generated by a multitude of devices contain a wealth of knowledge and serve as a critical resource for intelligent fault diagnosis and efficient system operations. In recent years, various deep learning methods and the pre-training...
Lanlan Rui, Yuan-Rui Yang, Peng Yu et al.· IEEE Transactions on Network...· 0 citations
In agentic AI-enabled edge computing, decentralized federated learning (DFL) leverages peer-to-peer model aggregation to improve the performance of on-device large language models (LLMs) without introducing a single point of failure, thereby enhancing local agents’ capabilities for decision-making. To protect the right...
Zhiqiang Xie, Yijing Lin, Zhipeng Gao et al.· IEEE Transactions on Cogniti...· 0 citations
Cloud service platforms are increasingly extended to cloud-edge continua to support latency-sensitive and computation-intensive applications. In such distributed service environments, heterogeneous and time-varying compute capacity across edge sites creates strong competition among users, making it difficult to jointly...
Yun Xia, Gang Zhou, Lirui Pan et al.· IEEE International Conferenc...· 0 citations
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