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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 301–310 of 536 papers

TitleStatusHype
LocalNewton: Reducing Communication Bottleneck for Distributed Learning—0
Innovation Compression for Communication-efficient Distributed Optimization with Linear Convergence—0
An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed OptimizationCode1
Improving the Transient Times for Distributed Stochastic Gradient Methods—0
Distributed Energy Trading Management for Renewable Prosumers with HVAC and Energy Storage—0
Mean Field MARL Based Bandwidth Negotiation Method for Massive Devices Spectrum Sharing—0
Distributed Experiment Design and Control for Multi-agent Systems with Gaussian Processes—0
Distributed Newton-like Algorithms and Learning for Optimized Power Dispatch—0
Efficient Randomized Subspace Embeddings for Distributed Optimization under a Communication BudgetCode0
Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable DevicesCode1
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