2 citations · 4 across the 4 of their papers we have counts for
12 papers · 1 filter
Private and Accurate Decentralized Optimization via Encrypted and Structured Functional Perturbation
Yijie Zhou, Shi Pu
We propose a decentralized optimization algorithm that preserves the privacy of agents' cost functions without sacrificing accuracy, termed EFPSN. The algorithm adopts Paillier cry…
A Compressed Gradient Tracking Method for Decentralized Optimization with Linear Convergence
Yiwei Liao, Zhuorui Li, Kun Huang +1
Communication compression techniques are of growing interests for solving the decentralized optimization problem under limited communication, where the global objective is to minim…
Compressed Gradient Tracking Methods for Decentralized Optimization with Linear Convergence
Yiwei Liao, Zhuorui Li, Kun Huang +1
Communication compression techniques are of growing interests for solving the decentralized optimization problem under limited communication, where the global objective is to minim…
A Robust Gradient Tracking Method for Distributed Optimization over Directed Networks
Shi Pu
In this paper, we consider the problem of distributed consensus optimization over multi-agent networks with directed network topology. Assuming each agent has a local cost function…
Asymptotic Network Independence in Distributed Stochastic Optimization for Machine Learning
Shi Pu, Alex Olshevsky, Ioannis Ch. Paschalidis
We provide a discussion of several recent results which, in certain scenarios, are able to overcome a barrier in distributed stochastic optimization for machine learning. Our focus…
A Sharp Estimate on the Transient Time of Distributed Stochastic Gradient Descent
Shi Pu, Alex Olshevsky, Ioannis Ch. Paschalidis
This paper is concerned with minimizing the average of cost functions over a network in which agents may communicate and exchange information with each other. We consider the s…