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 201–210 of 536 papers

TitleStatusHype
When Evolutionary Computation Meets Privacy—0
On the Convergence of Decentralized Federated Learning Under Imperfect Information SharingCode0
Byzantine-Robust Loopless Stochastic Variance-Reduced GradientCode0
Differentially Private Distributed Convex Optimization—0
Multi-Message Shuffled Privacy in Federated Learning—0
TAMUNA: Doubly Accelerated Distributed Optimization with Local Training, Compression, and Partial Participation—0
Distributed Optimization for Reactive Power Sharing and Stability of Inverter-Based Resources Under Voltage Limits—0
Sparse-SignSGD with Majority Vote for Communication-Efficient Distributed Learning—0
CEDAS: A Compressed Decentralized Stochastic Gradient Method with Improved Convergence—0
Algorithm Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks—0
Show:102550
← PrevPage 21 of 54Next →

No leaderboard results yet.