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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
Problem-dependent convergence bounds for randomized linear gradient compression—0
Logarithmically Quantized Distributed Optimization over Dynamic Multi-Agent Networks—0
Tighter Performance Theory of FedExProx—0
Byzantine-Resilient Output Optimization of Multiagent via Self-Triggered Hybrid Detection Approach—0
FedECADO: A Dynamical System Model of Federated Learning—0
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
Seamless Integration: Sampling Strategies in Federated Learning Systems—0
Prescribed-time Convergent Distributed Multiobjective Optimization with Dynamic Event-triggered Communication—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
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