Optimization
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels
State-of-the-art federated learning methods can perform far worse than their centralized counterparts when clients have dissimilar data distributions.
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels
State-of-the-art federated learning methods can perform far worse than their centralized counterparts when clients have dissimilar data distributions.
Global Convergence to Local Minmax Equilibrium in Classes of Nonconvex Zero-Sum Games
Prior to moving on, we briefly comment on and provide context for each contribution of this paper.
A Stochastic Linearized Augmented Lagrangian Method for Decentralized Bilevel Optimization Songtao Lu
Classical bilevel optimization is referred to as the case where there is no consensus constraint but with only two levels of the minimization subproblems, i.e.,
Local Linear Convergence of Gradient Methods for Subspace Optimization via Strict Complementarity
In this work we bridge these two approaches under a strict complementarity assumption, which in particular implies that the optimal solution to the convex relaxation is unique and is also the optimal solution to the original nonconvex problem.