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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 81–90 of 536 papers

TitleStatusHype
A Mirror Descent-Based Algorithm for Corruption-Tolerant Distributed Gradient Descent—0
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning—0
Information-Geometric Barycenters for Bayesian Federated Learning—0
Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums—0
Byzantine-Resilient Federated Learning via Distributed Optimization—0
Byzantine-Resilient Non-Convex Stochastic Gradient Descent—0
Byzantine-Resilient Output Optimization of Multiagent via Self-Triggered Hybrid Detection Approach—0
Algorithm Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks—0
Byzantine-Robust Learning on Heterogeneous Datasets via Resampling—0
Adaptive Consensus ADMM for Distributed Optimization—0
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