90 citations · 105 across the 6 of their papers we have counts for
6 papers
Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data
Xinting Liao, Chaochao Chen, Weiming Liu +7
Federated learning (FL) is a distributed machine learning paradigm that needs collaboration between a server and a series of clients with decentralized data. To make FL effective i…
Differentially Private Learning with Per-Sample Adaptive Clipping
Tianyu Xia, Shuheng Shen, Su Yao +4
Privacy in AI remains a topic that draws attention from researchers and the general public in recent years. As one way to implement privacy-preserving AI, differentially private le…
STL-SGD: Speeding Up Local SGD with Stagewise Communication Period
Shuheng Shen, Yifei Cheng, Jingchang Liu +1
Distributed parallel stochastic gradient descent algorithms are workhorses for large scale machine learning tasks. Among them, local stochastic gradient descent (Local SGD) has att…
Variance Reduced Local SGD with Lower Communication Complexity
Xianfeng Liang, Shuheng Shen, Jingchang Liu +3
To accelerate the training of machine learning models, distributed stochastic gradient descent (SGD) and its variants have been widely adopted, which apply multiple workers in para…
Faster Distributed Deep Net Training: Computation and Communication Decoupled Stochastic Gradient Descent
Shuheng Shen, Linli Xu, Jingchang Liu +2
With the increase in the amount of data and the expansion of model scale, distributed parallel training becomes an important and successful technique to address the optimization ch…
Asynchronous Stochastic Composition Optimization with Variance Reduction
Shuheng Shen, Linli Xu, Jingchang Liu +2
Composition optimization has drawn a lot of attention in a wide variety of machine learning domains from risk management to reinforcement learning. Existing methods solving the com…