21 citations · 78 across the 16 of their papers we have counts for
5 papers · 1 filter
FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data
Hao Sun, Li Shen, Shixiang Chen +4
Federated learning is an emerging distributed machine learning method, enables a large number of clients to train a model without exchanging their local data. The time cost of comm…
Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape
Yan Sun, Li Shen, Shixiang Chen +2
In federated learning (FL), a cluster of local clients are chaired under the coordination of the global server and cooperatively train one model with privacy protection. Due to the…
Decentralized Weakly Convex Optimization Over the Stiefel Manifold
Jinxin Wang, Jiang Hu, Shixiang Chen +2
We focus on a class of non-smooth optimization problems over the Stiefel manifold in the decentralized setting, where a connected network of agents cooperatively minimize a fin…
AdaSAM: Boosting Sharpness-Aware Minimization with Adaptive Learning Rate and Momentum for Training Deep Neural Networks
Hao Sun, Li Shen, Qihuang Zhong +6
Sharpness aware minimization (SAM) optimizer has been extensively explored as it can generalize better for training deep neural networks via introducing extra perturbation steps to…
OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System
Chao Xue, Wei Liu, Shuai Xie +25
Automated machine learning (AutoML) seeks to build ML models with minimal human effort. While considerable research has been conducted in the area of AutoML in general, aiming to t…