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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 301–350 of 536 papers

TitleStatusHype
Distributed Unmixing of Hyperspectral Data With Sparsity Constraint—0
Distributed Utility Optimization in Vehicular Communication Systems—0
Distributed Very Large Scale Bundle Adjustment by Global Camera Consensus—0
DOPE: Distributed Optimization for Pairwise Energies—0
Do Subsampled Newton Methods Work for High-Dimensional Data?—0
Double Quantization for Communication-Efficient Distributed Optimization—0
DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization—0
D-SVM over Networked Systems with Non-Ideal Linking Conditions—0
Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling—0
DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime—0
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections—0
Dynamic Incentive Strategies for Smart EV Charging Stations: An LLM-Driven User Digital Twin Approach—0
EControl: Fast Distributed Optimization with Compression and Error Control—0
Efficient Distributed Optimization under Heavy-Tailed Noise—0
ELM-Based Distributed Cooperative Learning Over Networks—0
End-to-End Quality-of-Service Assurance with Autonomous Systems: 5G/6G Case Study—0
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization—0
Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning—0
Estimating the Error of Randomized Newton Methods: A Bootstrap Approach—0
Estimation Network Design framework for efficient distributed optimization—0
Assessing the Impacts of Nonideal Communications on Distributed Optimal Power Flow Algorithms—0
Exploring Scaling Laws for Local SGD in Large Language Model Training—0
Fast Adaptive Federated Bilevel Optimization—0
Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty—0
Fast Distributed Optimization over Directed Graphs under Malicious Attacks using Trust—0
FedADMM: A Federated Primal-Dual Algorithm Allowing Partial Participation—0
FedECADO: A Dynamical System Model of Federated Learning—0
Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning—0
Federated Conditional Stochastic Optimization—0
Federated K-Means Clustering via Dual Decomposition-based Distributed Optimization—0
Federated Learning Assisted Distributed Energy Optimization—0
Federated Learning: From Theory to Practice—0
A Unified Linear Speedup Analysis of Federated Averaging and Nesterov FedAvg—0
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms—0
Federated Multi-Level Optimization over Decentralized Networks—0
Federated Optimization:Distributed Optimization Beyond the Datacenter—0
Federated Optimization: Distributed Machine Learning for On-Device Intelligence—0
Federated Optimization with Doubly Regularized Drift Correction—0
Federated TD Learning over Finite-Rate Erasure Channels: Linear Speedup under Markovian Sampling—0
FedSplit: An algorithmic framework for fast federated optimization—0
Finite-Time Consensus Learning for Decentralized Optimization with Nonlinear Gossiping—0
Distributed Optimization with Quantized Gradient Descent—0
Flattened one-bit stochastic gradient descent: compressed distributed optimization with controlled variance—0
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning—0
FL-MISR: Fast Large-Scale Multi-Image Super-Resolution for Computed Tomography Based on Multi-GPU Acceleration—0
Fractional Order Distributed Optimization—0
From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges—0
Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton—0
Fundamental Resource Trade-offs for Encoded Distributed Optimization—0
Generalized Gradient Descent is a Hypergraph Functor—0
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