3 citations · 3 across the 13 of their papers we have counts for
7 papers · 1 filter
ReLoRA: Knowledge-Reusing Adaptation for Fast Rollout of Evolving LLM Services
Yang Xu, Zihuai Xu, Hongli Xu +3
Large Language Models (LLMs) are increasingly deployed as continuously evolving services, where frequent base-model updates may invalidate previously deployed task-specific Low-Ran…
Resource-Efficient Federated Fine-Tuning Large Language Models for Heterogeneous Data
Jun Liu, Yunming Liao, Hongli Xu +1
Fine-tuning large language models (LLMs) via federated learning, i.e., FedLLM, has been proposed to adapt LLMs for various downstream applications in a privacy-preserving way. To r…
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…
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…