5 papers
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…
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…
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…