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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 151–200 of 536 papers

TitleStatusHype
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing—0
Debiased distributed learning for sparse partial linear models in high dimensions—0
Accelerating variational quantum algorithms with multiple quantum processors—0
Byzantine-Resilient Non-Convex Stochastic Gradient Descent—0
Distributed Averaging Methods for Randomized Second Order Optimization—0
Byzantine-Resilient Federated Learning via Distributed Optimization—0
Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums—0
Distributed Algorithms for Feature Extraction Off-loading in Multi-Camera Visual Sensor Networks—0
Byzantine Fault Tolerant Distributed Linear Regression—0
An Equivalent Circuit Approach to Distributed Optimization—0
Distributed Continuous-Time Optimization with Uncertain Time-Varying Quadratic Cost Functions—0
Discretized Distributed Optimization over Dynamic Digraphs—0
DiLoCo: Distributed Low-Communication Training of Language Models—0
Diffusion Adaptation over Networks—0
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning—0
Differentially-Private Distributed Model Predictive Control of Linear Discrete-Time Systems with Global Constraints—0
Differentially Private Distributed Convex Optimization—0
Differentially Private Consensus-Based Distributed Optimization—0
Distributed and time-varying primal-dual dynamics via contraction analysis—0
Distributed Asynchronous Dual Free Stochastic Dual Coordinate Ascent—0
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs—0
Distributed Collision-Free Motion Coordination on a Sphere: A Conic Control Barrier Function Approach—0
Distributed Convex Optimization With Limited Communications—0
Distributed Convex Optimization with State-Dependent (Social) Interactions over Random Networks—0
Distributed Coordination of Multi-Microgrids in Active Distribution Networks for Provisioning Ancillary Services—0
Distributed CPU Scheduling Subject to Nonlinear Constraints—0
Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets—0
Distributed Delay-Tolerant Strategies for Equality-Constraint Sum-Preserving Resource Allocation—0
Distributed Difference of Convex Optimization—0
Distributed Dual Quaternion Based Localization of Visual Sensor Networks—0
Accelerated Distributed Optimization with Compression and Error Feedback—0
Distributed Dynamic Safe Screening Algorithms for Sparse Regularization—0
Distributed Energy Trading Management for Renewable Prosumers with HVAC and Energy Storage—0
Distributed estimation of the inverse Hessian by determinantal averaging—0
Distributed Experiment Design and Control for Multi-agent Systems with Gaussian Processes—0
Distributed Fractional Bayesian Learning for Adaptive Optimization—0
Distributed gradient-based optimization in the presence of dependent aperiodic communication—0
Distributed gradient methods under heavy-tailed communication noise—0
Distributed Training of Graph Convolutional Networks—0
Distributed Graph Learning with Smooth Data Priors—0
Distributed Inexact Damped Newton Method: Data Partitioning and Load-Balancing—0
Detecting Shared Data Manipulation in Distributed Optimization Algorithms—0
Distributed Learning of Generalized Linear Causal Networks—0
Distributed Learning of Neural Lyapunov Functions for Large-Scale Networked Dissipative Systems—0
Distributed learning with compressed gradients—0
Distributed Linear Regression with Compositional Covariates—0
Design of heterogeneous multi-agent system for distributed computation—0
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning—0
Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications—0
Deep Reinforcement Learning for QoS-Constrained Resource Allocation in Multiservice Networks—0
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