3 citations · 6 across the 9 of their papers we have counts for
5 papers · 1 filter
SplitFT: An Adaptive Federated Split Learning System For LLMs Fine-Tuning
Yimeng Shan, Zhaorui Zhang, Sheng Di +3
Federated Split Learning has been identified as an efficient approach to address the computational resource constraints of clients in classical federated learning, while guaranteei…
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…
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…
A Survey on Error-Bounded Lossy Compression for Scientific Datasets
Sheng Di, Jinyang Liu, Kai Zhao +23
Error-bounded lossy compression has been effective in significantly reducing the data storage/transfer burden while preserving the reconstructed data fidelity very well. Many error…
An Optimized Error-controlled MPI Collective Framework Integrated with Lossy Compression
Jiajun Huang, Sheng Di, Xiaodong Yu +11
With the ever-increasing computing power of supercomputers and the growing scale of scientific applications, the efficiency of MPI collective communications turns out to be a criti…