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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 411–420 of 536 papers

TitleStatusHype
Data Encoding for Byzantine-Resilient Distributed Optimization—0
Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex OptimizationCode0
Asymptotic Network Independence in Distributed Stochastic Optimization for Machine Learning—0
Distributed Optimization for Smart Cyber-Physical Networks—0
Secure Architectures Implementing Trusted Coalitions for Blockchained Distributed Learning (TCLearn)—0
Distributed Optimization for Over-Parameterized Learning—0
The Communication Complexity of Optimization—0
Communication-Efficient Accurate Statistical Estimation—0
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations—0
Deep Learning for Distributed Optimization: Applications to Wireless Resource Management—0
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