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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 521–530 of 536 papers

TitleStatusHype
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning—0
Byzantine Fault Tolerant Distributed Linear Regression—0
Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums—0
Byzantine-Resilient Federated Learning via Distributed Optimization—0
Byzantine-Resilient Non-Convex Stochastic Gradient Descent—0
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
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