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Distributed Solvers for Network Linear Equations with Scalarized Compression
We then employ such a compressed consensus flow as a fundamental consensus subroutine to develop distributed continuous-time and discrete-time solvers for network linear equations, and prove their exponential convergence properties under scalar node communications.
Distributed Zeroth-Order Optimization: Convergence Rates That Match Centralized Counterpart
Zeroth-order optimization has become increasingly important in complex optimization and machine learning when cost functions are impossible to be described in closed analytical forms.
Differential Privacy with Manifold Data Dependency
In this paper, we study dataset processing mechanisms generated by linear queries in the presence of manifold data dependency.
Distributed Online Optimization with Long-Term Constraints
When the loss functions are strongly convex, we establish improved regret and constraint violation upper bounds in $\mathcal{O}(\log(T))$ and $\mathcal{O}(\sqrt{T\log(T)})$.
Distributed Online Linear Regression
We propose simple and natural distributed regression algorithms, involving, at each node and in each round, a local gradient descent step and a communication and averaging step where nodes aim at aligning their predictors to those of their neighbors.
