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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 31–40 of 536 papers

TitleStatusHype
SCAFFOLD: Stochastic Controlled Averaging for Federated LearningCode1
Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance ReductionCode1
Federated Optimization in Heterogeneous NetworksCode1
Communication Efficient, Differentially Private Distributed Optimization using Correlation-Aware Sketching—0
Multi-Timescale Gradient Sliding for Distributed Optimization—0
Decentralized Optimization on Compact Submanifolds by Quantized Riemannian Gradient Tracking—0
Distributed gradient methods under heavy-tailed communication noise—0
Online distributed optimization for spatio-temporally constrained real-time peer-to-peer energy trading—0
Privacy-Preserving Peer-to-Peer Energy Trading via Hybrid Secure Computations—0
Federated Learning: From Theory to Practice—0
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