11 citations · 26 across the 12 of their papers we have counts for
12 papers
ORBITALIF: An Efficient Spiking Federated Learning Framework for Onboard Cloud Removal
Bohan Zhang, Chenyu Xu, Yijie Mao +1
Low-earth-orbit (LEO) satellites enable high-resolution, large-scale Earth observation for applications such as disaster monitoring and environmental surveillance. However, cloud c…
Unseen Cost of Space Computing: Quantifying LEO Battery Aging via Physics-Driven Modeling
Li Zeng, Jingyang Zhu, Zixin Wang +2
Low Earth Orbit (LEO) satellite constellations in the 6G era are evolving into intelligent in-orbit computational platforms, forming Space Computing Power Networks (SCPNs) to deliv…
Service Function Chain Routing in LEO Networks Using Shortest-Path Delay Statistical Stability
Li Zeng, Zixin Wang, Yuanming Shi +1
Low Earth orbit (LEO) satellite constellations have become a critical enabler for global coverage, utilizing numerous satellites orbiting Earth at high speeds. By decomposing compl…
Pipeline Parallelism is All You Need for Optimized Early-Exit Based Self-Speculative Decoding
Ruanjun Li, Ziheng Liu, Yuanming Shi +3
Large language models (LLMs) deliver impressive generation quality, but incur very high inference cost because each output token is generated auto-regressively through all model la…
Edge Large AI Models: Collaborative Deployment and IoT Applications
Zixin Wang, Yuanming Shi, Khaled. B. Letaief
Large artificial intelligence models (LAMs) emulate human-like problem-solving capabilities across diverse domains, modalities, and tasks. By leveraging the communication and compu…
Edge Large AI Models: Revolutionizing 6G Networks
Zixin Wang, Yuanming Shi, Yong Zhou +2
Large artificial intelligence models (LAMs) possess human-like abilities to solve a wide range of real-world problems, exemplifying the potential of experts in various domains and…
Satellite Edge Artificial Intelligence with Large Models: Architectures and Technologies
Yuanming Shi, Jingyang Zhu, Chunxiao Jiang +2
Driven by the growing demand for intelligent remote sensing applications, large artificial intelligence (AI) models pre-trained on large-scale unlabeled datasets and fine-tuned for…
Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networks
Yan Zhu, Jingyang Zhu, Ting Wang +3
Advancements in artificial intelligence (AI) and low-earth orbit (LEO) satellites have promoted the application of large remote sensing foundation models for various downstream tas…
Structured IB: Improving Information Bottleneck with Structured Feature Learning
Hanzhe Yang, Youlong Wu, Dingzhu Wen +2
The Information Bottleneck (IB) principle has emerged as a promising approach for enhancing the generalization, robustness, and interpretability of deep neural networks, demonstrat…
Hierarchical Learning and Computing over Space-Ground Integrated Networks
Jingyang Zhu, Yuanming Shi, Yong Zhou +2
Space-ground integrated networks hold great promise for providing global connectivity, particularly in remote areas where large amounts of valuable data are generated by Internet o…
Federated Fine-Tuning for Pre-Trained Foundation Models Over Wireless Networks
Zixin Wang, Yong Zhou, Yuanming Shi +1
Pre-trained foundation models (FMs), with extensive number of neurons, are key to advancing next-generation intelligence services, where personalizing these models requires massive…
Satellite Federated Edge Learning: Architecture Design and Convergence Analysis
Yuanming Shi, Li Zeng, Jingyang Zhu +3
The proliferation of low-earth-orbit (LEO) satellite networks leads to the generation of vast volumes of remote sensing data which is traditionally transferred to the ground server…