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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 321–330 of 536 papers

TitleStatusHype
Fast Adaptive Federated Bilevel Optimization—0
Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty—0
Fast Distributed Optimization over Directed Graphs under Malicious Attacks using Trust—0
FedADMM: A Federated Primal-Dual Algorithm Allowing Partial Participation—0
FedECADO: A Dynamical System Model of Federated Learning—0
Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning—0
Federated Conditional Stochastic Optimization—0
Federated K-Means Clustering via Dual Decomposition-based Distributed Optimization—0
Federated Learning Assisted Distributed Energy Optimization—0
Federated Learning: From Theory to Practice—0
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