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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 111–120 of 536 papers

TitleStatusHype
Collaborative Learning over Wireless Networks: An Introductory Overview—0
Communication-Efficient Distributed Kalman Filtering using ADMM—0
A Plug and Play Distributed Secondary Controller for Microgrids with Grid-Forming Inverters—0
Communication-Efficient Distributed SGD with Compressed Sensing—0
Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies—0
Communication Efficient Federated Learning with Linear Convergence on Heterogeneous Data—0
Communication-Efficient Projection-Free Algorithm for Distributed Optimization—0
Communication-efficient Variance-reduced Stochastic Gradient Descent—0
Anytime MiniBatch: Exploiting Stragglers in Online Distributed Optimization—0
Centralised and Distributed Optimization for Aggregated Flexibility Services Provision—0
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