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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 61–70 of 536 papers

TitleStatusHype
Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement—0
AttentionX: Exploiting Consensus Discrepancy In Attention from A Distributed Optimization Perspective—0
GNN-Empowered Effective Partial Observation MARL Method for AoI Management in Multi-UAV NetworkCode1
Prescribed-time Convergent Distributed Multiobjective Optimization with Dynamic Event-triggered Communication—0
Seamless Integration: Sampling Strategies in Federated Learning Systems—0
A survey on secure decentralized optimization and learning—0
Distributed Optimization by Network Flows with Spatio-Temporal Compression—0
Spatio-Temporal Communication Compression in Distributed Prime-Dual Flows—0
Distributed Difference of Convex Optimization—0
A Mirror Descent-Based Algorithm for Corruption-Tolerant Distributed Gradient Descent—0
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