306 citations · 764 across the 49 of their papers we have counts for
5 papers · 1 filter
An Efficient Split Fine-tuning Framework for Edge and Cloud Collaborative Learning
Shaohuai Shi, Qing Yang, Yang Xiang +2
To enable the pre-trained models to be fine-tuned with local data on edge devices without sharing data with the cloud, we design an efficient split fine-tuning (SFT) framework for…
EASNet: Searching Elastic and Accurate Network Architecture for Stereo Matching
Qiang Wang, Shaohuai Shi, Kaiyong Zhao +1
Recent advanced studies have spent considerable human efforts on optimizing network architectures for stereo matching but hardly achieved both high accuracy and fast inference spee…
Scalable K-FAC Training for Deep Neural Networks with Distributed Preconditioning
Lin Zhang, Shaohuai Shi, Wei Wang +1
The second-order optimization methods, notably the D-KFAC (Distributed Kronecker Factored Approximate Curvature) algorithms, have gained traction on accelerating deep neural networ…
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning
Zhenheng Tang, Yonggang Zhang, Shaohuai Shi +3
In federated learning (FL), model performance typically suffers from client drift induced by data heterogeneity, and mainstream works focus on correcting client drift. We propose a…
Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters
Yang Xiang, Zhihua Wu, Weibao Gong +15
The ever-growing model size and scale of compute have attracted increasing interests in training deep learning models over multiple nodes. However, when it comes to training on clo…