The rapid proliferation of data-intensive and delay-sensitive applications has accelerated the evolution from centralized cloud computing to mobile edge-cloud systems. However, the decentralized and heterogeneous characteristics introduce instability and complexity, making efficient scheduling increasingly challenging....
Yun-Feng Zhao, Chao Qiu, Xiao-Fei Wang et al.· IEEE Transactions on Mobile...· 0 citations
Deploying federated learning (FL) in mobile edge computing (MEC) networks enables collaborative model training while preserving the privacy of raw data. However, due to system heterogeneity, statistical heterogeneity of mobile clients (MCs), and client dropout, optimizing bandwidth allocation and fine-tuning the global...
Jian Tang, Lu-Xi Cheng, Xiu-Hua Li et al.· IEEE Transactions on Mobile...· 0 citations
Retrieval-augmented generation (RAG) improves factuality by conditioning LLMs on retrieved evidence, yet real-world knowledge is often split across tiers: cloud-based RAG can exploit large public corpora, whereas edge-based RAG is the natural place to access private, user-specific stores. This raises a key question: ho...
Yuting Li, Shaoyuan Huang, Xiangqi Liu et al.· Proceedings of the 32nd ACM...· 0 citations
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