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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 101–110 of 536 papers

TitleStatusHype
Privacy-Preserving Distributed Optimization and Learning—0
Parallel Momentum Methods Under Biased Gradient Estimations—0
A Survey of Resilient Coordination for Cyber-Physical Systems Against Malicious Attacks—0
FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation RetrievalCode0
TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data—0
Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity—0
Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of MultipliersCode0
Survey of Distributed Algorithms for Resource Allocation over Multi-Agent Systems—0
Asynchronous Local-SGD Training for Language ModelingCode1
Optimal Data Splitting in Distributed Optimization for Machine Learning—0
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