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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 111–120 of 536 papers

TitleStatusHype
Correlated Quantization for Faster Nonconvex Distributed Optimization—0
A Proximal Gradient Method With Probabilistic Multi-Gossip Communications for Decentralized Composite Optimization—0
Convergence Visualizer of Decentralized Federated Distillation with Reduced Communication Costs—0
Asynchronous Distributed Optimization with Delay-free Parameters—0
A Distributed ADMM-based Deep Learning Approach for Thermal Control in Multi-Zone Buildings under Demand Response Events—0
Distributed Optimization via Kernelized Multi-armed Bandits—0
Federated Learning Assisted Distributed Energy Optimization—0
Leveraging Function Space Aggregation for Federated Learning at Scale—0
Short vs. Long-term Coordination of Drones: When Distributed Optimization Meets Deep Reinforcement Learning—0
DiLoCo: Distributed Low-Communication Training of Language Models—0
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