60 citations · 160 across the 10 of their papers we have counts for
4 papers · 1 filter
Dynamic Differential-Privacy Preserving SGD
Jian Du, Song Li, Xiangyi Chen +2
The vanilla Differentially-Private Stochastic Gradient Descent (DP-SGD), including DP-Adam and other variants, ensures the privacy of training data by uniformly distributing privac…
Toward Communication Efficient Adaptive Gradient Method
Xiangyi Chen, Xiaoyun Li, Ping Li
In recent years, distributed optimization is proven to be an effective approach to accelerate training of large scale machine learning models such as deep neural networks. With the…
On the Convergence of Decentralized Adaptive Gradient Methods
Xiangyi Chen, Belhal Karimi, Weijie Zhao +1
Adaptive gradient methods including Adam, AdaGrad, and their variants have been very successful for training deep learning models, such as neural networks. Meanwhile, given the nee…
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy
Xinwei Zhang, Xiangyi Chen, Mingyi Hong +2
Providing privacy protection has been one of the primary motivations of Federated Learning (FL). Recently, there has been a line of work on incorporating the formal privacy notion…