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 281–290 of 536 papers

TitleStatusHype
Private Multi-Task Learning: Formulation and Applications to Federated LearningCode0
FL-MISR: Fast Large-Scale Multi-Image Super-Resolution for Computed Tomography Based on Multi-GPU Acceleration—0
Dynamic communication topologies for distributed heuristics in energy system optimization algorithmsCode0
Distributed System Identification for Linear Stochastic Systems with Binary Sensors—0
Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time—0
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression—0
A Differential Private Method for Distributed Optimization in Directed Networks via State Decomposition—0
BAGUA: Scaling up Distributed Learning with System RelaxationsCode1
Robust Distributed Optimization With Randomly Corrupted Gradients—0
Accelerating variational quantum algorithms with multiple quantum processors—0
Show:102550
← PrevPage 29 of 54Next →

No leaderboard results yet.