8 papers
CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge
Zhiheng Guo, Zhaoyang Liu, Zihan Cen +5
The deployment of large-scale neural networks within the Open Radio Access Network (O-RAN) architecture is pivotal for enabling native edge intelligence. However, this paradigm fac…
Identifying and Evaluating Inactive Heads in Pretrained LLMs
Pedro Sandoval-Segura, Xijun Wang, Ashwinee Panda +4
Attention is foundational to large language models (LLMs), enabling different heads to have diverse focus on relevant input tokens. However, learned behaviors like attention sinks,…
Video Object Recognition in Mobile Edge Networks: Local Tracking or Edge Detection?
Kun Guo, Yun Shen, Xijun Wang +3
Fast and accurate video object recognition, which relies on frame-by-frame video analytics, remains a challenge for resource-constrained devices such as traffic cameras. Recent adv…
Accelerating Wireless Distributed Learning via Hybrid Split and Federated Learning Optimization
Kun Guo, Xuefei Li, Xijun Wang +3
Federated learning (FL) and split learning (SL) are two effective distributed learning paradigms in wireless networks, enabling collaborative model training across mobile devices w…
Analysis of SINR Coverage in LEO Satellite Networks through Spatial Network Calculus
Yuting Tang, Yufan He, Yi Zhong +3
We introduce a new analytical framework, developed based on the spatial network calculus, for performance assessment of Low Earth Orbit (LEO) satellite networks. Specifically, we m…
Toward 6G Native-AI Network: Foundation Model based Cloud-Edge-End Collaboration Framework
Xiang Chen, Zhiheng Guo, Xijun Wang +5
Future wireless communication networks are in a position to move beyond data-centric, device-oriented connectivity and offer intelligent, immersive experiences based on multi-agent…