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Distributed Optimization

The goal of Distributed Optimization is to optimize a certain objective defined over millions of billions of data that is distributed over many machines by utilizing the computational power of these machines.

Source: Analysis of Distributed StochasticDual Coordinate Ascent

Papers

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Showing 81–90 of 536 papers

TitleStatusHype
Single Point-Based Distributed Zeroth-Order Optimization with a Non-Convex Stochastic Objective Function—0
Aiding Global Convergence in Federated Learning via Local Perturbation and Mutual Similarity Information—0
A Federated Distributionally Robust Support Vector Machine with Mixture of Wasserstein Balls Ambiguity Set for Distributed Fault Diagnosis—0
Newton Meets Marchenko-Pastur: Massively Parallel Second-Order Optimization with Hessian Sketching and Debiasing—0
A Plug and Play Distributed Secondary Controller for Microgrids with Grid-Forming Inverters—0
Distributed Optimization via Energy Conservation Laws in Dilated Coordinates—0
Peer-to-Peer Learning Dynamics of Wide Neural Networks—0
Exploring Scaling Laws for Local SGD in Large Language Model Training—0
Distributed Optimization for Traffic Light Control and Connected Automated Vehicle Coordination in Mixed-Traffic Intersections—0
ADMM for Downlink Beamforming in Cell-Free Massive MIMO Systems—0
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