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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 1–50 of 536 papers

TitleStatusHype
Power Bundle Adjustment for Large-Scale 3D ReconstructionCode2
Byzantine-Robust Learning on Heterogeneous Datasets via BucketingCode1
Beyond spectral gap (extended): The role of the topology in decentralized learningCode1
Signal Decomposition Using Masked Proximal OperatorsCode1
Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical AnalysisCode1
DeepLM: Large-Scale Nonlinear Least Squares on Deep Learning Frameworks Using Stochastic Domain DecompositionCode1
Decentralized Riemannian Gradient Descent on the Stiefel ManifoldCode1
Federated Learning as Variational Inference: A Scalable Expectation Propagation ApproachCode1
Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable DevicesCode1
Secure Distributed Training at ScaleCode1
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?Code1
An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed OptimizationCode1
FedDANE: A Federated Newton-Type MethodCode1
Distributed Resource Allocation with Multi-Agent Deep Reinforcement Learning for 5G-V2V CommunicationCode1
Federated Optimization in Heterogeneous NetworksCode1
Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance ReductionCode1
DPLib: A Standard Benchmark Library for Distributed Power System Analysis and OptimizationCode1
Federated Accelerated Stochastic Gradient DescentCode1
Just One Byte (per gradient): A Note on Low-Bandwidth Decentralized Language Model Finetuning Using Shared RandomnessCode1
MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable ConvergenceCode1
Privacy-Preserving Distributed Optimization via Subspace Perturbation: A General FrameworkCode1
SCAFFOLD: Stochastic Controlled Averaging for Federated LearningCode1
Training Large Neural Networks with Constant Memory using a New Execution AlgorithmCode1
Unbiased Single-scale and Multi-scale Quantizers for Distributed OptimizationCode1
Optimization Algorithm Design via Electric CircuitsCode1
MANGO: A Python Library for Parallel Hyperparameter TuningCode1
Acceleration of Federated Learning with Alleviated Forgetting in Local TrainingCode1
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
FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated LearningCode1
Asynchronous Local-SGD Training for Language ModelingCode1
BAGUA: Scaling up Distributed Learning with System RelaxationsCode1
Beyond spectral gap: The role of the topology in decentralized learningCode1
A Federated Distributionally Robust Support Vector Machine with Mixture of Wasserstein Balls Ambiguity Set for Distributed Fault Diagnosis—0
Advances in Asynchronous Parallel and Distributed Optimization—0
A Dual Approach for Optimal Algorithms in Distributed Optimization over Networks—0
ADMM for Downlink Beamforming in Cell-Free Massive MIMO Systems—0
Accelerated consensus via Min-Sum Splitting—0
A Distributed Second-Order Algorithm You Can Trust—0
Acceleration in Distributed Optimization under Similarity—0
A Semi-Distributed Interior Point Algorithm for Optimal Coordination of Automated Vehicles at Intersections—0
Acceleration for Compressed Gradient Descent in Distributed Optimization—0
A Distributed ADMM-based Deep Learning Approach for Thermal Control in Multi-Zone Buildings under Demand Response Events—0
Accelerated Methods with Compressed Communications for Distributed Optimization Problems under Data Similarity—0
A Sequential Approximation Framework for Coded Distributed Optimization—0
A Stochastic Large-scale Machine Learning Algorithm for Distributed Features and Observations—0
A Novel Decentralized Algorithm for Coordinating the Optimal Power and Traffic Flows with EVs based on Variable Inner Loop Selection—0
Anytime MiniBatch: Exploiting Stragglers in Online Distributed Optimization—0
A Plug and Play Distributed Secondary Controller for Microgrids with Grid-Forming Inverters—0
An Online Optimization Approach for Multi-Agent Tracking of Dynamic Parameters in the Presence of Adversarial Noise—0
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