3 citations · 5 across the 3 of their papers we have counts for
5 papers
GT-STORM: Taming Sample, Communication, and Memory Complexities in Decentralized Non-Convex Learning
Xin Zhang, Jia Liu, Zhengyuan Zhu +1
Decentralized nonconvex optimization has received increasing attention in recent years in machine learning due to its advantages in system robustness, data privacy, and implementat…
Private and Communication-Efficient Edge Learning: A Sparse Differential Gaussian-Masking Distributed SGD Approach
Xin Zhang, Minghong Fang, Jia Liu +1
With rise of machine learning (ML) and the proliferation of smart mobile devices, recent years have witnessed a surge of interest in performing ML in wireless edge networks. In thi…
Communication-Efficient Network-Distributed Optimization with Differential-Coded Compressors
Xin Zhang, Jia Liu, Zhengyuan Zhu +1
Network-distributed optimization has attracted significant attention in recent years due to its ever-increasing applications. However, the classic decentralized gradient descent (D…
Byzantine-Resilient Stochastic Gradient Descent for Distributed Learning: A Lipschitz-Inspired Coordinate-wise Median Approach
Haibo Yang, Xin Zhang, Minghong Fang +1
In this work, we consider the resilience of distributed algorithms based on stochastic gradient descent (SGD) in distributed learning with potentially Byzantine attackers, who coul…
Distributed Linear Model Clustering over Networks: A Tree-Based Fused-Lasso ADMM Approach
Xin Zhang, Jia Liu, Zhengyuan Zhu
In this work, we consider to improve the model estimation efficiency by aggregating the neighbors' information as well as identify the subgroup membership for each node in the netw…