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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 301–325 of 536 papers

TitleStatusHype
LocalNewton: Reducing Communication Bottleneck for Distributed Learning—0
Innovation Compression for Communication-efficient Distributed Optimization with Linear Convergence—0
An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed OptimizationCode1
Improving the Transient Times for Distributed Stochastic Gradient Methods—0
Distributed Energy Trading Management for Renewable Prosumers with HVAC and Energy Storage—0
Mean Field MARL Based Bandwidth Negotiation Method for Massive Devices Spectrum Sharing—0
Distributed Experiment Design and Control for Multi-agent Systems with Gaussian Processes—0
Distributed Newton-like Algorithms and Learning for Optimized Power Dispatch—0
Efficient Randomized Subspace Embeddings for Distributed Optimization under a Communication BudgetCode0
Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable DevicesCode1
Gradient-Tracking over Directed Graphs for solving Leaderless Multi-Cluster Games—0
Decentralized Riemannian Gradient Descent on the Stiefel ManifoldCode1
Distributed Second Order Methods with Fast Rates and Compressed Communication—0
Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization—0
Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers—0
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning—0
Concentration of Non-Isotropic Random Tensors with Applications to Learning and Empirical Risk Minimization—0
Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications—0
Design of heterogeneous multi-agent system for distributed computation—0
Convergent Adaptive Gradient Methods in Decentralized Optimization—0
Cost-efficient SVRG with Arbitrary Sampling—0
Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning—0
Byzantine-Resilient Non-Convex Stochastic Gradient Descent—0
Linear Convergence of Distributed Mirror Descent with Integral Feedback for Strongly Convex Problems—0
Wyner-Ziv Estimators for Distributed Mean Estimation with Side Information and OptimizationCode0
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