36 citations · 80 across the 24 of their papers we have counts for
24 papers
Communication-Efficient Decentralized Multi-Agent Reinforcement Learning for Cooperative Adaptive Cruise Control
Dong Chen, Kaixiang Zhang, Yongqiang Wang +3
Connected and autonomous vehicles (CAVs) promise next-gen transportation systems with enhanced safety, energy efficiency, and sustainability. One typical control strategy for CAVs…
Locally Differentially Private Distributed Online Learning with Guaranteed Optimality
Ziqin Chen, Yongqiang Wang
Distributed online learning is gaining increased traction due to its unique ability to process large-scale datasets and streaming data. To address the growing public awareness and…
Decentralized Nonconvex Optimization with Guaranteed Privacy and Accuracy
Yongqiang Wang, Tamer Basar
Privacy protection and nonconvexity are two challenging problems in decentralized optimization and learning involving sensitive data. Despite some recent advances addressing each o…
Gradient-tracking Based Differentially Private Distributed Optimization with Enhanced Optimization Accuracy
Yu Xuan, Yongqiang Wang
Privacy protection has become an increasingly pressing requirement in distributed optimization. However, equipping distributed optimization with differential privacy, the state-of-…
A Robust Dynamic Average Consensus Algorithm that Ensures both Differential Privacy and Accurate Convergence
Yongqiang Wang
We propose a new dynamic average consensus algorithm that is robust to information-sharing noise arising from differential-privacy design. Not only is dynamic average consensus wid…
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…
Ensure Differential Privacy and Convergence Accuracy in Consensus Tracking and Aggregative Games with Coupling Constraints
Yongqiang Wang
We address differential privacy for fully distributed aggregative games with shared coupling constraints. By co-designing the generalized Nash equilibrium (GNE) seeking mechanism a…
Ensuring both Provable Convergence and Differential Privacy in Nash Equilibrium Seeking on Directed Graphs
Yongqiang Wang, Tamer Basar
We study in this paper privacy protection in fully distributed Nash equilibrium seeking where a player can only access its own cost function and receive information from its immedi…
Differentially-private Distributed Algorithms for Aggregative Games with Guaranteed Convergence
Yongqiang Wang, Angelia Nedich
The distributed computation of a Nash equilibrium in aggregative games is gaining increased traction in recent years. Of particular interest is the mediator-free scenario where ind…
Quantization enabled Privacy Protection in Decentralized Stochastic Optimization
Yongqiang Wang, Tamer Basar
By enabling multiple agents to cooperatively solve a global optimization problem in the absence of a central coordinator, decentralized stochastic optimization is gaining increasin…
Dynamics based Privacy Preservation in Decentralized Optimization
Huan Gao, Yongqiang Wang, Angelia Nedić
With decentralized optimization having increased applications in various domains ranging from machine learning, control, sensor networks, to robotics, its privacy is also receiving…
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…
Decentralized Gradient Methods with Time-varying Uncoordinated Stepsizes: Convergence Analysis and Privacy Design
Yongqiang Wang, Angelia Nedic
Decentralized optimization enables a network of agents to cooperatively optimize an overall objective function without a central coordinator and is gaining increased attention in d…
Tailoring Gradient Methods for Differentially-Private Distributed Optimization
Yongqiang Wang, Angelia Nedic
Decentralized optimization is gaining increased traction due to its widespread applications in large-scale machine learning and multi-agent systems. The same mechanism that enables…
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 Nonlinear Cloud-based Model Predictive Control via Affine Masking
Kaixiang Zhang, Zhaojian Li, Yongqiang Wang +1
With the advent of 5G technology that presents enhanced communication reliability and ultra low latency, there is renewed interest in employing cloud computing to perform high perf…
Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic
Dong Chen, Mohammad Hajidavalloo, Zhaojian Li +4
On-ramp merging is a challenging task for autonomous vehicles (AVs), especially in mixed traffic where AVs coexist with human-driven vehicles (HDVs). In this paper, we formulate th…
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…
On the Global Synchronization of Pulse-coupled Oscillators Interacting on Chain and Directed Tree Graphs
Huan Gao, Yongqiang Wang
Driven by increased applications in biological networks and wireless sensor networks, synchronization of pulse-coupled oscillators (PCOs) has gained increased popularity. However,…
Dynamics Based Privacy Protection for Average Consensus on Directed Graphs
Huan Gao, Yongqiang Wang
Average consensus is key for distributed networks, with applications ranging from network synchronization, distributed information fusion, decentralized control, to load balancing…
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…
ADMM Based Privacy-preserving Decentralized Optimization
Chunlei Zhang, Muaz Ahmad, Yongqiang Wang
Privacy preservation is addressed for decentralized optimization, where agents cooperatively minimize the sum of convex functions private to these individual agents. In mos…
Secure and Privacy-Preserving Average Consensus
Minghao Ruan, Muaz Ahmad, Yongqiang Wang
Average consensus is fundamental for distributed systems since it underpins key functionalities of such systems ranging from distributed information fusion, decision-making, to dec…