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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 141–150 of 536 papers

TitleStatusHype
Byzantine-Robust Learning on Heterogeneous Datasets via Resampling—0
An Exact Quantized Decentralized Gradient Descent Algorithm—0
Byzantine-Resilient Output Optimization of Multiagent via Self-Triggered Hybrid Detection Approach—0
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing—0
Debiased distributed learning for sparse partial linear models in high dimensions—0
Accelerating variational quantum algorithms with multiple quantum processors—0
Byzantine-Resilient Non-Convex Stochastic Gradient Descent—0
Byzantine-Resilient Federated Learning via Distributed Optimization—0
Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums—0
Byzantine Fault Tolerant Distributed Linear Regression—0
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