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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 41–50 of 536 papers

TitleStatusHype
Information-Geometric Barycenters for Bayesian Federated Learning—0
Deep Distributed Optimization for Large-Scale Quadratic Programming—0
Fractional Order Distributed Optimization—0
Review of Mathematical Optimization in Federated Learning—0
Problem-dependent convergence bounds for randomized linear gradient compression—0
Optimization Algorithm Design via Electric CircuitsCode1
Logarithmically Quantized Distributed Optimization over Dynamic Multi-Agent Networks—0
Tighter Performance Theory of FedExProx—0
Byzantine-Resilient Output Optimization of Multiagent via Self-Triggered Hybrid Detection Approach—0
FedECADO: A Dynamical System Model of Federated Learning—0
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