6 citations · 12 across the 10 of their papers we have counts for
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cs.LG2024★ 1 cited
MoDULA: Mixture of Domain-Specific and Universal LoRA for Multi-Task Learning
Yufei Ma, Zihan Liang, Huangyu Dai +9
The growing demand for larger-scale models in the development of \textbf{L}arge \textbf{L}anguage \textbf{M}odels (LLMs) poses challenges for efficient training within limited comp…
cs.LG2022★ 2 cited
Fast Heterogeneous Federated Learning with Hybrid Client Selection
Guangyuan Shen, Dehong Gao, Duanxiao Song +5
Client selection schemes are widely adopted to handle the communication-efficient problems in recent studies of Federated Learning (FL). However, the large variance of the model up…
cs.LG2022★ 6 cited
Variance-Reduced Heterogeneous Federated Learning via Stratified Client Selection
Guangyuan Shen, Dehong Gao, Libin Yang +4
Client selection strategies are widely adopted to handle the communication-efficient problem in recent studies of Federated Learning (FL). However, due to the large variance of the…