81 citations · 181 across the 16 of their papers we have counts for
16 papers
Cross-region Model Training with Communication-Computation Overlapping and Delay Compensation
Ying Zhu, Yang Xu, Hongli Xu +3
Training large language models (LLMs) requires massive computational resources, often necessitating the aggregation of geographically distributed data centers (\ie, cross-region tr…
A Novel Hat-Shaped Device-Cloud Collaborative Inference Framework for Large Language Models
Zuan Xie, Yang Xu, Hongli Xu +2
Recent advancements in large language models (LLMs) have catalyzed a substantial surge in demand for LLM services. While traditional cloud-based LLM services satisfy high-accuracy…
Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models
Zihuai Xu, Yang Xu, Hongli Xu +3
Considering the hardware-friendly characteristics and broad applicability, structured pruning has emerged as an efficient solution to reduce the resource demands of large language…
Efficient Deployment of Large Language Models on Resource-constrained Devices
Zhiwei Yao, Yang Xu, Hongli Xu +2
Deploying Large Language Models (LLMs) on resource-constrained (or weak) devices presents significant challenges due to limited resources and heterogeneous data distribution. To ad…
Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices
Jun Liu, Yunming Liao, Hongli Xu +3
Federated fine-tuning (FedFT) has been proposed to fine-tune the pre-trained language models in a distributed manner. However, there are two critical challenges for efficient FedFT…
Collaborative Inference for Large Models with Task Offloading and Early Exiting
Zuan Xie, Yang Xu, Hongli Xu +2
In 5G smart cities, edge computing is employed to provide nearby computing services for end devices, and the large-scale models (e.g., GPT and LLaMA) can be deployed at the network…
ParallelSFL: A Novel Split Federated Learning Framework Tackling Heterogeneity Issues
Yunming Liao, Yang Xu, Hongli Xu +3
Mobile devices contribute more than half of the world's web traffic, providing massive and diverse data for powering various federated learning (FL) applications. In order to avoid…
MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation
Yunming Liao, Yang Xu, Hongli Xu +3
Recently, federated learning (FL) has emerged as a popular technique for edge AI to mine valuable knowledge in edge computing (EC) systems. To mitigate the computing/communication…
SemiSFL: Split Federated Learning on Unlabeled and Non-IID Data
Yang Xu, Yunming Liao, Hongli Xu +3
Federated Learning (FL) has emerged to allow multiple clients to collaboratively train machine learning models on their private data at the network edge. However, training and depl…
Dataset Management Platform for Machine Learning
Ze Mao, Yang Xu, Erick Suarez
The quality of the data in a dataset can have a substantial impact on the performance of a machine learning model that is trained and/or evaluated using the dataset. Effective data…
Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learning
Zhida Jiang, Yang Xu, Hongli Xu +2
Federated learning (FL) allows multiple clients cooperatively train models without disclosing local data. However, the existing works fail to address all these practical concerns i…
Adaptive Configuration for Heterogeneous Participants in Decentralized Federated Learning
Yunming Liao, Yang Xu, Hongli Xu +2
Data generated at the network edge can be processed locally by leveraging the paradigm of edge computing (EC). Aided by EC, decentralized federated learning (DFL), which overcomes…
System size dependence of transverse momentum correlations at 62.4 and 200 GeV at the BNL Relativistic Heavy Ion Collider
STAR Collaboration, L. Adamczyk, J. K. Adkins +363
We present a study of the average transverse momentum () fluctuations and correlations for charged particles produced in Cu+Cu collisions at midrapidity for $\sqrt{s_{NN…
Anomalous centrality evolution of two-particle angular correlations from Au-Au collisions at = 62 and 200 GeV
STAR Collaboration, G. Agakishiev, M. M. Aggarwal +350
We present two-dimensional (2D) two-particle angular correlations on relative pseudorapidity and azimuth for charged particles from Au-Au collisions at $\sqrt{s_{\rm NN}} =…
Measurement of the Bottom contribution to non-photonic electron production in collisions at =200 GeV
STAR Collaboration, M. M. Aggarwal
The contribution of meson decays to non-photonic electrons, which are mainly produced by the semi-leptonic decays of heavy flavor mesons, in collisions at 20…
System size dependence of associated yields in hadron-triggered jets
STAR Collaboration, B. I. Abelev
We present results on the system size dependence of high transverse momentum di-hadron correlations at = 200 GeV as measured by STAR at RHIC. Measurements in d+Au,…