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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 261–270 of 536 papers

TitleStatusHype
Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning—0
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing—0
Distributed saddle point problems for strongly concave-convex functions—0
Spatial Reuse in Dense Wireless Areas: A Cross-layer Optimization Approach via ADMM—0
Communication Efficient Federated Learning via Ordered ADMM in a Fully Decentralized Setting—0
DASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity, and No Client Synchronization—0
Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning—0
End-to-End Quality-of-Service Assurance with Autonomous Systems: 5G/6G Case Study—0
Distributed gradient-based optimization in the presence of dependent aperiodic communication—0
Distributed Learning of Generalized Linear Causal Networks—0
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