Author

Victor C. M. Leung

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#federated learning Nov 2026

Accelerating Federated Learning Under Client Dropout via Joint Bandwidth Allocation and Prototype Fine-Tuning in Mobile Edge Computing Networks

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

#large language models Sep 2026

E<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="feng-ieq1-3676689.gif"/></alternatives></inline-formula>LLM: Structure-Guided Efficient Inference for LLMs in Distributed Edge

Large language models (LLMs) are increasingly deployed in edge computing environments to reduce latency and preserve privacy. However, their inference process presents fundamental challenges for resource-constrained IoT devices. LLM inference involves computationally asymmetric stages: parallelizable prompt processing...

Xingyu Feng, Huanqi Yang, Zhuangzhuang Chen et al. · IEEE Transactions on Mobile... · 0 citations