14 citations · 36 across the 12 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
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
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…
cs.LG2022★ 14 cited
AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight Aggregation
Qiyuan Wang, Qianqian Yang, Shibo He +2
In an asynchronous federated learning framework, the server updates the global model once it receives an update from a client instead of waiting for all the updates to arrive as in…