81 citations · 181 across the 16 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…