Federated Learning (FL) is an emerging distributed machine learning paradigm that protects data privacy by performing iterative local training and gradient aggregation across multiple devices and a central server. Over-the-air computation enables fast aggregation when multiple devices need to upload their local gradien...
Shi-Yuan Zuo, Rong-Fei Fan, Pu-Ning Zhao et al.· IEEE Transactions on Mobile...· 0 citations
The network virtualization (NV) technology has enabled the sharing of multiple resources among virtual networks (VNs) in cloud data centers. One of the key challenges is to allocate resources in real-time for virtual network request (VNR), which is known as online virtual network embedding (VNE). However, the existing...
Bin-Quan Guo, Zhou Zhang, Jun-Feng Zhai et al.· IEEE International Symposium...· 0 citations
Recently, computing Power Networks (CPNs) have emerged as a critical infrastructure for supporting intelligent services, particularly Large Language Models (LLMs). While integrating renewable energy is imperative for carbon neutrality, it faces significant challenges due to energy intermittency and the rigid reliabilit...
Ren-Chao Xie, Shuo Li, Qin-Qin Tang et al.· IEEE Transactions on Cogniti...· 0 citations
This work proposes a blockchain-enhanced edge AIGC service framework over MEC networks, where multiple edge servers act as both model hosts generating contents for MUs and miners participating in blockchain consensus, aiming to maximize the total system utility.
Licheng Ye, Ze-Hui Xiong, Yuan Luo et al.· IEEE Transactions on Network...· 0 citations
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