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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 126–150 of 536 papers

TitleStatusHype
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression—0
MUSIC: Accelerated Convergence for Distributed Optimization With Inexact and Exact Methods—0
Privacy-Preserving Distributed Optimization and Learning—0
Parallel Momentum Methods Under Biased Gradient Estimations—0
A Survey of Resilient Coordination for Cyber-Physical Systems Against Malicious Attacks—0
FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation RetrievalCode0
TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data—0
Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity—0
Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of MultipliersCode0
Survey of Distributed Algorithms for Resource Allocation over Multi-Agent Systems—0
Optimal Data Splitting in Distributed Optimization for Machine Learning—0
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
Discretized Distributed Optimization over Dynamic Digraphs—0
Optimally Managing the Impacts of Convergence Tolerance for Distributed Optimal Power Flow—0
Asynchronous Message-Passing and Zeroth-Order Optimization Based Distributed Learning with a Use-Case in Resource Allocation in Communication Networks—0
EControl: Fast Distributed Optimization with Compression and Error Control—0
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