3 citations · 4 across the 14 of their papers we have counts for
14 papers
DySTop
Yizhou Shi, Qianpiao Ma, Yan Xu +4
Federated Learning (FL) has emerged as a potential distributed learning paradigm that enables model training on edge devices (i.e., workers) while preserving data privacy. However,…
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…
Efficient Federated Fine-Tuning of Large Language Models with Layer Dropout
Shilong Wang, Jianchun Liu, Hongli Xu +2
Fine-tuning plays a crucial role in enabling pre-trained LLMs to evolve from general language comprehension to task-specific expertise. To preserve user data privacy, federated fin…
Collaborative Speculative Inference for Efficient LLM Inference Serving
Luyao Gao, Jianchun Liu, Hongli Xu +3
Speculative inference is a promising paradigm employing small speculative models (SSMs) as drafters to generate draft tokens, which are subsequently verified in parallel by the tar…
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…
A Robust Federated Learning Framework for Undependable Devices at Scale
Shilong Wang, Jianchun Liu, Hongli Xu +4
In a federated learning (FL) system, many devices, such as smartphones, are often undependable (e.g., frequently disconnected from WiFi) during training. Existing FL frameworks alw…
Caesar: A Low-deviation Compression Approach for Efficient Federated Learning
Jiaming Yan, Jianchun Liu, Hongli Xu +4
Compression is an efficient way to relieve the tremendous communication overhead of federated learning (FL) systems. However, for the existing works, the information loss under com…
Enhancing Federated Graph Learning via Adaptive Fusion of Structural and Node Characteristics
Xianjun Gao, Jianchun Liu, Hongli Xu +2
Federated Graph Learning (FGL) has demonstrated the advantage of training a global Graph Neural Network (GNN) model across distributed clients using their local graph data. Unlike…
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…
Top-: Not All Logits Are You Need
Chenxia Tang, Jianchun Liu, Hongli Xu +1
Large language models (LLMs) typically employ greedy decoding or low-temperature sampling for reasoning tasks, reflecting a perceived trade-off between diversity and accuracy. We c…
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…