6 citations · 13 across the 5 of their papers we have counts for
6 papers
Fast and Memory Efficient Offload Training Framework with Hybrid XPU Computation
Zhiyi Yao, Zuning Liang, Yuedong Xu +3
With the ever-growing size of deep learning models, GPU memory is prone to being insufficient during training. A prominent approach is ZeRO-Offload, which moves the optimizer state…
Amoeba: Runtime Tensor Parallel Transformation for LLM Inference Services
Haoyu Chen, Xue Li, Kun Qian +3
In Large Language Model (LLM) inference services, it is challenging to make a parallelism strategy configuration, to efficiently process the requests of variance context lengths. R…
A Satellite-Ground Synergistic Large Vision-Language Model System for Earth Observation
Yuxin Zhang, Jiahao Yang, Zhe Chen +3
Recently, large vision-language models (LVLMs) unleash powerful analysis capabilities for low Earth orbit (LEO) satellite Earth observation images in the data center. However, fast…
LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Data
Yuxin Zhang, Haoyu Chen, Zheng Lin +2
Clustered federated learning (CFL) addresses the performance challenges posed by data heterogeneity in federated learning (FL) by organizing edge devices with similar data distribu…
SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework
Yuxin Zhang, Zheng Lin, Zhe Chen +5
Traditional federated learning (FL) frameworks rely heavily on terrestrial networks, where coverage limitations and increasing bandwidth congestion significantly hinder model conve…
FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data
Yuxin Zhang, Haoyu Chen, Zheng Lin +2
Clustered federated learning (CFL) is proposed to mitigate the performance deterioration stemming from data heterogeneity in federated learning (FL) by grouping similar clients for…