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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 201–225 of 536 papers

TitleStatusHype
When Evolutionary Computation Meets Privacy—0
On the Convergence of Decentralized Federated Learning Under Imperfect Information SharingCode0
Byzantine-Robust Loopless Stochastic Variance-Reduced GradientCode0
Differentially Private Distributed Convex Optimization—0
Multi-Message Shuffled Privacy in Federated Learning—0
TAMUNA: Doubly Accelerated Distributed Optimization with Local Training, Compression, and Partial ParticipationCode0
Distributed Optimization for Reactive Power Sharing and Stability of Inverter-Based Resources Under Voltage Limits—0
Sparse-SignSGD with Majority Vote for Communication-Efficient Distributed Learning—0
CEDAS: A Compressed Decentralized Stochastic Gradient Method with Improved Convergence—0
Algorithm Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks—0
Machine Learning for Large-Scale Optimization in 6G Wireless Networks—0
Decentralized Stochastic Multi-Player Multi-Armed Walking Bandits—0
Simulation-Integrated Distributed Optimal Power Flow for Unbalanced Power Distribution Systems—0
BALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with Application to Distributed Optimization—0
Continual Learning with Distributed Optimization: Does CoCoA Forget?—0
Simple and Scalable Algorithms for Cluster-Aware Precision Medicine—0
Distributed Optimization with Quantized Gradient Descent—0
Impact of Redundancy on Resilience in Distributed Optimization and Learning—0
Fast Adaptive Federated Bilevel Optimization—0
GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational ComplexityCode0
Provably Doubly Accelerated Federated Learning: The First Theoretically Successful Combination of Local Training and Communication Compression—0
Distributed MPC for Self-Organized Cooperation of Multiagent Systems -- Extended Version—0
Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence—0
Shuffle-QUDIO: accelerate distributed VQE with trainability enhancement and measurement reductionCode0
Cooperative Tuning of Multi-Agent Optimal Control SystemsCode0
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