3 citations · 6 across the 9 of their papers we have counts for
4 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…
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…
FedFa: A Fully Asynchronous Training Paradigm for Federated Learning
Haotian Xu, Zhaorui Zhang, Sheng Di +3
Federated learning has been identified as an efficient decentralized training paradigm for scaling the machine learning model training on a large number of devices while guaranteei…