3 citations · 6 across the 9 of their papers we have counts for
5 papers · 1 filter
An Efficient Gradient-Aware Error-Bounded Lossy Compressor for Federated Learning
Zhijing Ye, Sheng Di, Jiamin Wang +3
Federated learning (FL) enables collaborative model training without exposing clients' private data, but its deployment is often constrained by the communication cost of transmitti…
MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model?
Songkai Ma, Zhaorui Zhang, Sheng Di +4
With the widespread application of Mixture of Experts (MoE) reasoning models in the field of LLM learning, efficiently serving MoE models under limited GPU memory constraints has e…
HLoRA: Efficient Federated Learning System for LLM Heterogeneous Fine-Tuning
Qianli Liu, Zhaorui Zhang, Xin Yao +1
Federated learning systems have been identified as an efficient approach to scaling distributed model training with a large amount of participants or data owners while guaranteeing…
CLLoRA: An Approach to Measure the Effects of the Context Length for LLM Fine-Tuning
Ping Zhang, Zhaorui Zhang, Sheng Di +2
Large language model fine-tuning has been identified as an efficient approach to applying the pre-trained Large language models to other domains. To guarantee data privacy for diff…
ZCCL: Significantly Improving Collective Communication With Error-Bounded Lossy Compression
Jiajun Huang, Sheng Di, Xiaodong Yu +12
With the ever-increasing computing power of supercomputers and the growing scale of scientific applications, the efficiency of MPI collective communication turns out to be a critic…