SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 71–80 of 536 papers

TitleStatusHype
Accelerated Distributed Optimization with Compression and Error Feedback—0
99% of Distributed Optimization is a Waste of Time: The Issue and How to Fix it—0
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs—0
Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets—0
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning—0
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning—0
BALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with Application to Distributed Optimization—0
A Mirror Descent-Based Algorithm for Corruption-Tolerant Distributed Gradient Descent—0
Information-Geometric Barycenters for Bayesian Federated Learning—0
Algorithm Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks—0
Show:102550
← PrevPage 8 of 54Next →

No leaderboard results yet.