4 citations · 6 across the 8 of their papers we have counts for
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cs.LG2024★ 4 cited
FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition
Yuxuan Yan, Qianqian Yang, Shunpu Tang +1
Despite their exceptional performance on various tasks after fine-tuning, pre-trained language models (PLMs) face significant challenges due to growing privacy concerns with data i…
cs.LG2023★ 2 cited
Confidant: Customizing Transformer-based LLMs via Collaborative Edge Training
Yuhao Chen, Yuxuan Yan, Qianqian Yang +3
Transformer-based large language models (LLMs) have demonstrated impressive capabilities in a variety of natural language processing (NLP) tasks. Nonetheless, it is challenging to…
cs.LG2023
AccEPT: An Acceleration Scheme for Speeding Up Edge Pipeline-parallel Training
Yuhao Chen, Yuxuan Yan, Qianqian Yang +4
It is usually infeasible to fit and train an entire large deep neural network (DNN) model using a single edge device due to the limited resources. To facilitate intelligent applica…