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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 526–536 of 536 papers

TitleStatusHype
Byzantine-Resilient Output Optimization of Multiagent via Self-Triggered Hybrid Detection Approach—0
Byzantine-Robust Learning on Heterogeneous Datasets via Resampling—0
Can Competition Outperform Collaboration? The Role of Misbehaving Agents—0
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression—0
CEC: Crowdsourcing-based Evolutionary Computation for Distributed Optimization—0
CEDAS: A Compressed Decentralized Stochastic Gradient Method with Improved Convergence—0
Cell Zooming with Masked Data for Off-Grid Small Cell Networks: Distributed Optimization Approach—0
Centralised and Distributed Optimization for Aggregated Flexibility Services Provision—0
Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies—0
Collaborative Learning over Wireless Networks: An Introductory Overview—0
Collaborative Satisfaction of Long-Term Spatial Constraints in Multi-Agent Systems: A Distributed Optimization Approach (extended version)—0
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