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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 76–100 of 536 papers

TitleStatusHype
Communication-Efficient Accurate Statistical Estimation—0
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning—0
BALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with Application to Distributed Optimization—0
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs—0
A Mirror Descent-Based Algorithm for Corruption-Tolerant Distributed Gradient Descent—0
Information-Geometric Barycenters for Bayesian Federated Learning—0
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning—0
Algorithm Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks—0
Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums—0
Byzantine-Resilient Federated Learning via Distributed Optimization—0
Byzantine-Resilient Non-Convex Stochastic Gradient Descent—0
Byzantine-Resilient Output Optimization of Multiagent via Self-Triggered Hybrid Detection Approach—0
Adaptive Consensus ADMM for Distributed Optimization—0
Byzantine-Robust Learning on Heterogeneous Datasets via Resampling—0
An Exact Quantized Decentralized Gradient Descent Algorithm—0
Can Competition Outperform Collaboration? The Role of Misbehaving Agents—0
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression—0
CEC: Crowdsourcing-based Evolutionary Computation for Distributed Optimization—0
CEDAS: A Compressed Decentralized Stochastic Gradient Method with Improved Convergence—0
Cell Zooming with Masked Data for Off-Grid Small Cell Networks: Distributed Optimization Approach—0
Centralised and Distributed Optimization for Aggregated Flexibility Services Provision—0
Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies—0
Anytime MiniBatch: Exploiting Stragglers in Online Distributed Optimization—0
Collaborative Learning over Wireless Networks: An Introductory Overview—0
Auction-based and Distributed Optimization Approaches for Scheduling Observations in Satellite Constellations with Exclusive Orbit Portions—0
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