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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
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
Beyond spectral gap (extended): The role of the topology in decentralized learningCode1
Beyond spectral gap: The role of the topology in decentralized learningCode1
Acceleration of Federated Learning with Alleviated Forgetting in Local TrainingCode1
Signal Decomposition Using Masked Proximal OperatorsCode1
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?Code1
Unbiased Single-scale and Multi-scale Quantizers for Distributed OptimizationCode1
BAGUA: Scaling up Distributed Learning with System RelaxationsCode1
Secure Distributed Training at ScaleCode1
DeepLM: Large-Scale Nonlinear Least Squares on Deep Learning Frameworks Using Stochastic Domain DecompositionCode1
An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed OptimizationCode1
Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable DevicesCode1
Decentralized Riemannian Gradient Descent on the Stiefel ManifoldCode1
Distributed Resource Allocation with Multi-Agent Deep Reinforcement Learning for 5G-V2V CommunicationCode1
Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical AnalysisCode1
Byzantine-Robust Learning on Heterogeneous Datasets via BucketingCode1
Federated Accelerated Stochastic Gradient DescentCode1
MANGO: A Python Library for Parallel Hyperparameter TuningCode1
Privacy-Preserving Distributed Optimization via Subspace Perturbation: A General FrameworkCode1
Training Large Neural Networks with Constant Memory using a New Execution AlgorithmCode1
FedDANE: A Federated Newton-Type MethodCode1
SCAFFOLD: Stochastic Controlled Averaging for Federated LearningCode1
Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance ReductionCode1
Federated Optimization in Heterogeneous NetworksCode1
Communication Efficient, Differentially Private Distributed Optimization using Correlation-Aware Sketching—0
Multi-Timescale Gradient Sliding for Distributed Optimization—0
Decentralized Optimization on Compact Submanifolds by Quantized Riemannian Gradient Tracking—0
Distributed gradient methods under heavy-tailed communication noise—0
Online distributed optimization for spatio-temporally constrained real-time peer-to-peer energy trading—0
Privacy-Preserving Peer-to-Peer Energy Trading via Hybrid Secure Computations—0
Federated Learning: From Theory to Practice—0
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs—0
LAGO: Few-shot Crosslingual Embedding Inversion Attacks via Language Similarity-Aware Graph Optimization—0
Distributed Optimization with Efficient Communication, Event-Triggered Solution Enhancement, and Operation Stopping—0
Hessian Riemannian Flow For Multi-Population Wardrop Equilibrium—0
Residual-Evasive Attacks on ADMM in Distributed Optimization—0
Distributed model predictive control without terminal cost under inexact distributed optimization—0
Distributed Optimization with Gradient Tracking over Heterogeneous Delay-Prone Directed Networks—0
Graph Neural Network-Based Distributed Optimal Control for Linear Networked Systems: An Online Distributed Training Approach—0
Reducing the Communication of Distributed Model Predictive Control: Autoencoders and Formation Control—0
Dynamic Incentive Strategies for Smart EV Charging Stations: An LLM-Driven User Digital Twin Approach—0
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