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 1–10 of 536 papers

TitleStatusHype
Power Bundle Adjustment for Large-Scale 3D ReconstructionCode2
DPLib: A Standard Benchmark Library for Distributed Power System Analysis and OptimizationCode1
FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated LearningCode1
Optimization Algorithm Design via Electric CircuitsCode1
GNN-Empowered Effective Partial Observation MARL Method for AoI Management in Multi-UAV NetworkCode1
ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM TrainingCode1
MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable ConvergenceCode1
Asynchronous Local-SGD Training for Language ModelingCode1
Just One Byte (per gradient): A Note on Low-Bandwidth Decentralized Language Model Finetuning Using Shared RandomnessCode1
Federated Learning as Variational Inference: A Scalable Expectation Propagation ApproachCode1
Show:102550
← PrevPage 1 of 54Next →

No leaderboard results yet.