The rapid growth of edge cloud infrastructure introduces new challenges in managing energy consumption data while ensuring privacy during energy trading. Existing studies focused on smart metering privacy, local electricity markets, and privacy-aware control, whereas privacy-preserving cloudlet energy trading with batt...
Li-Wan Qi, Li Xiong, Bo-Chun Wu et al.· IEEE Transactions on Network...· 0 citations
Diffusion language models (DLMs) offer a non-autoregressive alternative for mobile edge agentic artificial intelligence (AI) by refining tokens through iterative denoising rather than left-to-right decoding. Compared with autoregressive Transformer-based large language models (LLMs), DLMs can update multiple uncertain...
Chen-Qi Li, Ming-Hui Min, D. Niyato et al.· 0 citations
This paper analyzes the DePIN technology stack from six layers: physical infrastructure, blockchain, interaction, trust, incentive, and application, with special attention to their cross-layer feedback loops, implementation readiness, and deployment limitations.
Ming Jiang, Er-Wu Liu, Xin-Yu Qu et al.· IEEE Communications Surveys...· 0 citations
This work proposes a split-inference framework for implementing CVL, which fully leverages the distributed communication and computing resources of the 6G SAGIN architecture and discusses potential research directions for 6G SAGIN-enabled CVL.
Min Hao, Yan-Bing Xu, Maoqiang Wu et al.· arXiv.org· 0 citations
Achieving reliable network-wide consensus formation in distributed learning systems becomes increasingly challenging when edge nodes hold skewed data distributions. Federated learning (FL) enables privacy-preserving collaborative model training without sharing raw data, but statistical heterogeneity across nodes signif...
Xinyu Qu, Shaoyi Han, Erwu Liu et al.· IEEE Transactions on Network...· 1 citation
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