4 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…