activity
20222026
most citedEfficient Wireless Federated Learning with Partial Model Aggregation

5 citations · 7 across the 8 of their papers we have counts for

collaborators

8 papers

cs.DC2026★ 1 cited

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities

Zhixiong Chen, Bingjie Zhu, Jiangzhou Wang +3

Large language models (LLMs) have advanced rapidly, emerging as versatile tools across fields thanks to their exceptional language understanding, generation, and reasoning capabili…

eess.SP2026

Sampling-Free Diffusion Transformers for Low-Complexity MIMO Channel Estimation

Zhixiong Chen, Hyundong Shin, Arumugam Nallanathan

Diffusion model-based channel estimators have shown impressive performance but suffer from high computational complexity because they rely on iterative reverse sampling. This paper…

eess.SY2025

Efficient LLM Inference over Heterogeneous Edge Networks with Speculative Decoding

Bingjie Zhu, Zhixiong Chen, Liqiang Zhao +2

Large language model (LLM) inference at the network edge is a promising serving paradigm that leverages distributed edge resources to run inference near users and enhance privacy.…

eess.SP2025

Large Language Model-Empowered Channel Prediction and Predictive Beamforming for LEO Satellite Communications

Zhixiong Chen, Hyundong Shin, Arumugam Nallanathan +1

Accurate channel prediction and effective beamforming are essential for low Earth orbit (LEO) satellite communications to enhance system capacity and enable high-speed connectivity…

eess.SY2025

Large Language Model-Empowered Decision Transformer for UAV-Enabled Data Collection

Zhixion Chen, Jiangzhou Wang, Hyundong Shin +1

The deployment of unmanned aerial vehicles (UAVs) for reliable and energy-efficient data collection from spatially distributed devices holds great promise in supporting diverse Int…

eess.SP2022

Knowledge-aided Federated Learning for Energy-limited Wireless Networks

Zhixiong Chen, Wenqiang Yi, Yuanwei Liu +1

The conventional model aggregation-based federated learning (FL) approach requires all local models to have the same architecture, which fails to support practical scenarios with h…

eess.SP2022★ 1 cited

Dynamic Task Software Caching-assisted Computation Offloading for Multi-Access Edge Computing

Zhixiong Chen, Wenqiang Yi, Atm S. Alam +1

In multi-access edge computing (MEC), most existing task software caching works focus on statically caching data at the network edge, which may hardly preserve high reusability due…

cs.LG2022★ 5 cited

Efficient Wireless Federated Learning with Partial Model Aggregation

Zhixiong Chen, Wenqiang Yi, Arumugam Nallanathan +1

The data heterogeneity across devices and the limited communication resources, e.g., bandwidth and energy, are two of the main bottlenecks for wireless federated learning (FL). To…