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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 181–190 of 536 papers

TitleStatusHype
TAMUNA: Doubly Accelerated Distributed Optimization with Local Training, Compression, and Partial Participation—0
Distributed Optimization for Reactive Power Sharing and Stability of Inverter-Based Resources Under Voltage Limits—0
Sparse-SignSGD with Majority Vote for Communication-Efficient Distributed Learning—0
Federated Learning as Variational Inference: A Scalable Expectation Propagation ApproachCode1
CEDAS: A Compressed Decentralized Stochastic Gradient Method with Improved Convergence—0
Algorithm Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks—0
Beyond spectral gap (extended): The role of the topology in decentralized learningCode1
Machine Learning for Large-Scale Optimization in 6G Wireless Networks—0
Decentralized Stochastic Multi-Player Multi-Armed Walking Bandits—0
Simulation-Integrated Distributed Optimal Power Flow for Unbalanced Power Distribution Systems—0
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