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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 291–300 of 536 papers

TitleStatusHype
Distributed Optimization using Heterogeneous Compute SystemsCode0
Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation AnalysisCode0
Distributed Online Optimization with Byzantine Adversarial Agents—0
Toward Communication Efficient Adaptive Gradient Method—0
Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees—0
On the Convergence of Decentralized Adaptive Gradient Methods—0
The Minimax Complexity of Distributed Optimization—0
Private Multi-Task Learning: Formulation and Applications to Federated LearningCode0
FL-MISR: Fast Large-Scale Multi-Image Super-Resolution for Computed Tomography Based on Multi-GPU Acceleration—0
Dynamic communication topologies for distributed heuristics in energy system optimization algorithmsCode0
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