2 citations · 3 across the 16 of their papers we have counts for
17 papers
Workflow-Aware Communication and Computation Scheduling for Mobile Edge AI Agents
Xingqiu He, Chaoqun You, Feng Wang +3
AI agents are increasingly deployed in mobile edge environments to autonomously handle diverse tasks through multi-step workflows. Unlike conventional dependency-aware tasks with p…
Rethinking the Foundations of Two-Sided AI Models for 6G
Yongjeong Oh, Zihan Chen, Timothy J. O'Shea +4
For next-generation air interfaces, two-sided artificial intelligence (AI) models have received growing attention, with AI models deployed at both the transmitter and receiver for…
Loss-Resilient Wireless Video Token Communication over Block Fading Channels
Bingyan Xie, Yongjeong Oh, Zihan Chen +3
Video token communication represents video content as discrete tokens that differ in their importance to reconstruction and exhibit temporal dependencies. When these tokens are pac…
Media Meets Communication in 6G: Fundamentals, Key Technologies, and Applications
Bingyan Xie, Longyu Zhou, Zihan Chen +9
The rapid advancement of sixth-generation (6G) networks is accelerating the convergence of media intelligence and communication intelligence, driving media communication beyond con…
Communication-Efficient Digital-Twin Coordination for Heterogeneous LLM Embodied Agents over Computing Power Networks
Nuocheng Yang, Sihua Wang, Zihan Chen +2
Embodied agent teams powered by heterogeneous large language models (LLMs) are being widely deployed in physical artificial intelligence such as smart factories, warehouses, and se…
Priority-Aware Learning-Unlearning Correction for Dynamic Decentralized LoRA Fine-Tuning
Nuocheng Yang, Yechen He, Sihua Wang +3
As large language models (LLMs) are increasingly deployed at the network edge to provide pervasive generative AI services, decentralized federated learning (DFL) provides a vital m…
Energy-Efficient Online Scheduling for Wireless Powered Mobile Edge Computing Networks
Xingqiu He, Chaoqun You, Yuzhi Yang +4
Wireless Powered Mobile Edge Computing (WP-MEC) integrates mobile edge computing (MEC) with wireless power transfer (WPT) to simultaneously extend the operational lifetime and enha…
Federated Inference for Heterogeneous LLM Communication and Collaboration
Zihan Chen, Zeshen Li, Howard H. Yang +2
Given the limited performance and efficiency of on-device Large Language Models (LLMs), the collaborations between multiple LLMs enable desirable performance enhancements, in which…
Hyperion: Hierarchical Scheduling for Parallel LLM Acceleration in Multi-tier Networks
Mulei Ma, Xinyi Xu, Minrui Xu +3
LLMs are increasingly executed in edge where limited GPU memory and heterogeneous computation jointly constrain deployment which motivates model partitioning and request scheduling…
Towards Secure Semantic Transmission In the Era of GenAI: A Diffusion-based Framework
Boxiang He, Zihan Chen, Junshan Luo +4
Semantic communication, due to its focus on the transmitting meaning rather than the raw bit data, poses unique security challenges compared to the traditional communication system…
Diffusion-enabled Secure Semantic Communication Against Eavesdropping
Boxiang He, Zihan Chen, Fanggang Wang +3
In this paper, AN is introduced into semantic communication systems for the first time to prevent semantic eavesdropping. However, the introduction of AN also poses challenges for…
Robust Federated Learning Over the Air: Combating Heavy-Tailed Noise with Median Anchored Clipping
Jiaxing Li, Zihan Chen, Kai Fong Ernest Chong +3
Leveraging over-the-air computations for model aggregation is an effective approach to cope with the communication bottleneck in federated edge learning. By exploiting the superpos…
Adaptive Federated Learning Over the Air
Chenhao Wang, Zihan Chen, Nikolaos Pappas +3
We propose a federated version of adaptive gradient methods, particularly AdaGrad and Adam, within the framework of over-the-air model training. This approach capitalizes on the in…
Spectral Co-Distillation for Personalized Federated Learning
Zihan Chen, Howard H. Yang, Tony Q. S. Quek +1
Personalized federated learning (PFL) has been widely investigated to address the challenge of data heterogeneity, especially when a single generic model is inadequate in satisfyin…
Value of Information and Timing-aware Scheduling for Federated Learning
Muhammad Azeem Khan, Howard H. Yang, Zihan Chen +2
Data possesses significant value as it fuels advancements in AI. However, protecting the privacy of the data generated by end-user devices has become crucial. Federated Learning (F…
Joint User Association and Resource Allocation for Adaptive Semantic Communication in 5G and Beyond Networks
Xingqiu He, Chaoqun You, Zihan Chen +4
Semantic communication (SemCom) has emerged as a promising paradigm that leverages Deep Neural Networks (DNNs) to extract task-relevant information, thereby substantially reducing…
The Role of Federated Learning in a Wireless World with Foundation Models
Zihan Chen, Howard H. Yang, Y. C. Tay +2
Foundation models (FMs) are general-purpose artificial intelligence (AI) models that have recently enabled multiple brand-new generative AI applications. The rapid advances in FMs…