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20112026
most citedRethinking Modern Communication from Semantic Coding to Semantic Communication

142 citations · 487 across the 83 of their papers we have counts for

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12 papers · 1 filter

cs.LG2026

In-Context Source and Channel Coding

Ziqiong Wang, Tianqi Ren, Rongpeng Li +2

Separate Source-Channel Coding (SSCC) remains attractive for text transmission due to its modularity and compatibility with mature entropy coders and powerful channel codes. Howeve…

cs.LG2025

Pareto Actor-Critic for Communication and Computation Co-Optimization in Non-Cooperative Federated Learning Services

Renxuan Tan, Rongpeng Li, Xiaoxue Yu +3

Federated learning (FL) in multi-service provider (SP) ecosystems is fundamentally hampered by non-cooperative dynamics, where privacy constraints and competing interests preclude…

cs.LG2025

AirLLM: Diffusion Policy-based Adaptive LoRA for Remote Fine-Tuning of LLM over the Air

Shiyi Yang, Xiaoxue Yu, Rongpeng Li +3

Operating Large Language Models (LLMs) on edge devices is increasingly challenged by limited communication bandwidth and strained computational and memory costs. Thus, cloud-assist…

cs.LG2025★ 1 cited

Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learning

Chongyang Tan, Ruoqi Wen, Rongpeng Li +3

Federated Learning (FL) enables distributed model training across edge devices in a privacy-friendly manner. However, its efficiency heavily depends on effective device selection a…

cs.LG2024

MERLOT: A Distilled LLM-based Mixture-of-Experts Framework for Scalable Encrypted Traffic Classification

Yuxuan Chen, Rongpeng Li, Zhifeng Zhao +1

We present MERLOT, a scalable mixture-of-expert (MoE) based refinement of distilled large language model optimized for encrypted traffic classification. By applying model distillat…

cs.LG2024★ 3 cited

Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach

Yuxuan Chen, Rongpeng Li, Xiaoxue Yu +2

Optimizing the deployment of large language models (LLMs) in edge computing environments is critical for enhancing privacy and computational efficiency. Toward efficient wireless L…