13 citations · 23 across the 5 of their papers we have counts for
5 papers
Analyzing Communication Predictability in LLM Training
Wenxue Li, Xiangzhou Liu, Yuxuan Li +9
Effective communication is essential in distributed training, with predictability being one of its most significant characteristics. However, existing studies primarily focus on ex…
MixNet: A Runtime Reconfigurable Optical-Electrical Fabric for Distributed Mixture-of-Experts Training
Xudong Liao, Yijun Sun, Han Tian +13
Mixture-of-Expert (MoE) models outperform conventional models by selectively activating different subnets, named experts, on a per-token basis. This gated computation generates dyn…
VERTICES: Efficient Two-Party Vertical Federated Linear Model with TTP-aided Secret Sharing
Mingxuan Fan, Yilun Jin, Liu Yang +2
Vertical Federated Learning (VFL) has emerged as one of the most predominant approaches for secure collaborative machine learning where the training data is partitioned by features…
Design and Operation of Shared Machine Learning Clusters on Campus
Kaiqiang Xu, Decang Sun, Hao Wang +6
Amid the rapid advancements in large machine learning (ML) models, universities worldwide are investing substantial funds and efforts into GPU clusters. However, managing a shared…
Secure Machine Learning over Relational Data
Qiyao Luo, Yilei Wang, Zhenghang Ren +3
A closer integration of machine learning and relational databases has gained steam in recent years due to the fact that the training data to many ML tasks is the results of a relat…