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Federated Optimization with Doubly Regularized Drift Correction
Federated learning is a distributed optimization paradigm that allows training machine learning models across decentralized devices while keeping the data localized.
Non-Convex Stochastic Composite Optimization with Polyak Momentum
In this paper, we focus on the stochastic proximal gradient method with Polyak momentum.
Polynomial Preconditioning for Gradient Methods
We study first-order methods with preconditioning for solving structured nonlinear convex optimization problems.
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