2 citations · 5 across the 5 of their papers we have counts for
6 papers
Gradient-tracking based Distributed Optimization with Guaranteed Optimality under Noisy Information Sharing
Yongqiang Wang, Tamer Başar
Distributed optimization enables networked agents to cooperatively solve a global optimization problem even with each participating agent only having access to a local partial view…
Decentralized Stochastic Optimization with Inherent Privacy Protection
Yongqiang Wang, H. Vincent Poor
Decentralized stochastic optimization is the basic building block of modern collaborative machine learning, distributed estimation and control, and large-scale sensing. Since invol…
Algorithm-Level Confidentiality for Average Consensus on Time-Varying Directed Graphs
Huan Gao, Yongqiang Wang
Average consensus plays a key role in distributed networks, with applications ranging from time synchronization, information fusion, load balancing, to decentralized control. Exist…
Privacy-Preserving Dynamic Average Consensus via State Decomposition: Case Study on Multi-Robot Formation Control
Kaixiang Zhang, Zhaojian Li, Yongqiang Wang +2
Dynamic average consensus is a decentralized control/estimation framework where a group of agents cooperatively track the average of local time-varying reference signals. In this p…
Privacy-Preserved Collaborative Estimation for Networked Vehicles with Application to Road Anomaly Detection
Huan Gao, Zhaojian Li, Yongqiang Wang
Road information such as road profile and traffic density have been widely used in intelligent vehicle systems to improve road safety, ride comfort, and fuel economy. However, vehi…
Privacy-preserving Decentralized Optimization via Decomposition
Chunlei Zhang, Huan Gao, Yongqiang Wang
This paper considers the problem of privacy-preservation in decentralized optimization, in which agents cooperatively minimize a global objective function that is the sum of $N…