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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
Federated Learning: Challenges, Methods, and Future DirectionsCode0
CoCoA: A General Framework for Communication-Efficient Distributed OptimizationCode0
Distributed Adversarial Training to Robustify Deep Neural Networks at ScaleCode0
Asymptotic Network Independence in Distributed Stochastic Optimization for Machine Learning—0
A survey on secure decentralized optimization and learning—0
A Federated Distributionally Robust Support Vector Machine with Mixture of Wasserstein Balls Ambiguity Set for Distributed Fault Diagnosis—0
A Survey on Distributed Evolutionary Computation—0
A Survey of Resilient Coordination for Cyber-Physical Systems Against Malicious Attacks—0
Advances in Asynchronous Parallel and Distributed Optimization—0
A Survey of Optimization Methods for Training DL Models: Theoretical Perspective on Convergence and Generalization—0
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