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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 21–30 of 536 papers

TitleStatusHype
Beyond spectral gap: The role of the topology in decentralized learningCode1
Byzantine-Robust Learning on Heterogeneous Datasets via BucketingCode1
Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical AnalysisCode1
MANGO: A Python Library for Parallel Hyperparameter TuningCode1
Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable DevicesCode1
Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance ReductionCode1
Acceleration of Federated Learning with Alleviated Forgetting in Local TrainingCode1
DeepLM: Large-Scale Nonlinear Least Squares on Deep Learning Frameworks Using Stochastic Domain DecompositionCode1
ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM TrainingCode1
SCAFFOLD: Stochastic Controlled Averaging for Federated LearningCode1
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