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20232026
most citedSpectral Co-Distillation for Personalized Federated Learning

2 citations · 3 across the 16 of their papers we have counts for

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5 papers · 1 filter

cs.NI2026

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…

cs.NI2026

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…

cs.NI2023

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…

cs.NI2023★ 1 cited

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…

cs.NI2023

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…