1 citations · 1 across the 7 of their papers we have counts for
8 papers
CoLLM: Continuous Adaptation for SLO-Aware LLM Serving on Shared GPU Clusters
Shaoyuan Huang, Yunfeng Zhao, Na Yan +5
As Large Language Models (LLMs) are increasingly adopted in edge intelligence to power domain-specific applications and personalized services, the quality and efficiency of the LLM…
Joint Scheduling of Multi-Band Radar Sensing and DNN Inference for Cross-Stage Parallelism
Yanan Du, Sai Xu, Kezhi Wang +1
This paper studies end-to-end latency minimization for a multi-band radar sensing and deep neural network (DNN) inference pipeline. Unlike conventional stage-wise designs that trea…
NetWorld: Communication-Based Diffusion World Model for Multi-Agent Reinforcement Learning in Wireless Networks
Kechen Meng, Rongpeng Li, Yansha Deng +2
As wireless communication networks grow in scale and complexity, diverse resource allocation tasks become increasingly critical. Multi-Agent Reinforcement Learning (MARL) provides…
Multi-Agent Conditional Diffusion Model with Mean Field Communication as Wireless Resource Allocation Planner
Kechen Meng, Sinuo Zhang, Rongpeng Li +5
In wireless communication systems, efficient and adaptive resource allocation plays a crucial role in enhancing overall Quality of Service (QoS). Compared to the conventional Model…
TT-Prune: Joint Model Pruning and Resource Allocation for Communication-efficient Time-triggered Federated Learning
Xinlu Zhang, Yansha Deng, Toktam Mahmoodi
Federated learning (FL) offers new opportunities in machine learning, particularly in addressing data privacy concerns. In contrast to conventional event-based federated learning,…
Exhaled Breath Analysis Through the Lens of Molecular Communication: A Survey
Sunasheer Bhattacharjee, Dadi Bi, Pit Hofmann +10
Molecular Communication (MC) has long been envisioned to enable an Internet of Bio-Nano Things (IoBNT) with medical applications, where nanomachines within the human body conduct m…
Joint Model Pruning and Resource Allocation for Wireless Time-triggered Federated Learning
Xinlu Zhang, Yansha Deng, Toktam Mahmoodi
Time-triggered federated learning, in contrast to conventional event-based federated learning, organizes users into tiers based on fixed time intervals. However, this network still…
Federated Reinforcement Learning for Uplink Centric Broadband Communication Optimization over Unlicensed Spectrum
Hui Zhou, Yansha Deng
To provide Uplink Centric Broadband Communication (UCBC), New Radio Unlicensed (NR-U) network has been standardized to exploit the unlicensed spectrum using Listen Before Talk (LBT…