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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 51–100 of 536 papers

TitleStatusHype
Single Point-Based Distributed Zeroth-Order Optimization with a Non-Convex Stochastic Objective Function—0
Aiding Global Convergence in Federated Learning via Local Perturbation and Mutual Similarity Information—0
A Federated Distributionally Robust Support Vector Machine with Mixture of Wasserstein Balls Ambiguity Set for Distributed Fault Diagnosis—0
Newton Meets Marchenko-Pastur: Massively Parallel Second-Order Optimization with Hessian Sketching and Debiasing—0
A Plug and Play Distributed Secondary Controller for Microgrids with Grid-Forming Inverters—0
Distributed Optimization via Energy Conservation Laws in Dilated Coordinates—0
Peer-to-Peer Learning Dynamics of Wide Neural Networks—0
Exploring Scaling Laws for Local SGD in Large Language Model Training—0
Distributed Optimization for Traffic Light Control and Connected Automated Vehicle Coordination in Mixed-Traffic Intersections—0
ADMM for Downlink Beamforming in Cell-Free Massive MIMO Systems—0
Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement—0
AttentionX: Exploiting Consensus Discrepancy In Attention from A Distributed Optimization Perspective—0
GNN-Empowered Effective Partial Observation MARL Method for AoI Management in Multi-UAV NetworkCode1
Prescribed-time Convergent Distributed Multiobjective Optimization with Dynamic Event-triggered Communication—0
Seamless Integration: Sampling Strategies in Federated Learning Systems—0
A survey on secure decentralized optimization and learning—0
Distributed Optimization by Network Flows with Spatio-Temporal Compression—0
Spatio-Temporal Communication Compression in Distributed Prime-Dual Flows—0
Distributed Difference of Convex Optimization—0
A Mirror Descent-Based Algorithm for Corruption-Tolerant Distributed Gradient Descent—0
Cooperative Integrated Sensing and Communication Networks: Analysis and Distributed Design—0
Communication- and Computation-Efficient Distributed Submodular Optimization in Robot Mesh NetworksCode0
Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization—0
Fast Distributed Optimization over Directed Graphs under Malicious Attacks using Trust—0
Graphon Particle Systems, Part II: Dynamics of Distributed Stochastic Continuum Optimization—0
Accelerating Distributed Optimization: A Primal-Dual Perspective on Local Steps—0
Graph Neural Networks Gone Hogwild—0
Distributed Utility Optimization in Vehicular Communication Systems—0
A KL-based Analysis Framework with Applications to Non-Descent Optimization Methods—0
ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM TrainingCode1
Log-Scale Quantization in Distributed First-Order Methods: Gradient-based Learning from Distributed Data—0
Local Methods with Adaptivity via Scaling—0
Differentially-Private Distributed Model Predictive Control of Linear Discrete-Time Systems with Global Constraints—0
MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable ConvergenceCode1
The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication—0
Flattened one-bit stochastic gradient descent: compressed distributed optimization with controlled variance—0
Structured Reinforcement Learning for Incentivized Stochastic Covert Optimization—0
Distributed Traffic Signal Control via Coordinated Maximum Pressure-plus-Penalty—0
Estimation Network Design framework for efficient distributed optimization—0
Rate Analysis of Coupled Distributed Stochastic Approximation for Misspecified Optimization—0
Distributed Fractional Bayesian Learning for Adaptive Optimization—0
Federated Optimization with Doubly Regularized Drift Correction—0
PIM-Opt: Demystifying Distributed Optimization Algorithms on a Real-World Processing-In-Memory SystemCode0
Generalized Gradient Descent is a Hypergraph Functor—0
Distributed Maximum Consensus over Noisy Links—0
Network-Aware Value Stacking of Community Battery via Asynchronous Distributed Optimization—0
Quantization Avoids Saddle Points in Distributed Optimization—0
Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction—0
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression—0
MUSIC: Accelerated Convergence for Distributed Optimization With Inexact and Exact Methods—0
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