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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 151–160 of 536 papers

TitleStatusHype
An Equivalent Circuit Approach to Distributed Optimization—0
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning—0
Differentially Private Consensus-Based Distributed Optimization—0
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs—0
Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets—0
Accelerated Distributed Optimization with Compression and Error Feedback—0
Detecting Shared Data Manipulation in Distributed Optimization Algorithms—0
Design of heterogeneous multi-agent system for distributed computation—0
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning—0
Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications—0
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