1 citations · 1 across the 5 of their papers we have counts for
6 papers
Accelerating Optimization and Machine Learning through Decentralization
Ziqin Chen, Zuang Wang, Yongqiang Wang
Decentralized optimization enables multiple devices to learn a global machine learning model while each individual device only has access to its local dataset. By avoiding the need…
Gradient Manipulation in Distributed Stochastic Gradient Descent with Strategic Agents: Truthful Incentives with Convergence Guarantees
Ziqin Chen, Yongqiang Wang
Distributed learning has gained significant attention due to its advantages in scalability, privacy, and fault tolerance.In this paradigm, multiple agents collaboratively train a g…
Local Differential Privacy for Distributed Stochastic Aggregative Optimization with Guaranteed Optimality
Ziqin Chen, Yongqiang Wang
Distributed aggregative optimization underpins many cooperative optimization and multi-agent control systems, where each agent's objective function depends both on its local optimi…
Ensuring Truthfulness in Distributed Aggregative Optimization
Ziqin Chen, Magnus Egerstedt, Yongqiang Wang
Distributed aggregative optimization methods are gaining increased traction due to their ability to address cooperative control and optimization problems, where the objective funct…
Privacy-Preserving Distributed Optimization and Learning
Ziqin Chen, Yongqiang Wang
Distributed optimization and learning has recently garnered great attention due to its wide applications in sensor networks, smart grids, machine learning, and so forth. Despite ra…
Locally Differentially Private Gradient Tracking for Distributed Online Learning over Directed Graphs
Ziqin Chen, Yongqiang Wang
Distributed online learning has been proven extremely effective in solving large-scale machine learning problems over streaming data. However, information sharing between learners…