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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 21–30 of 536 papers

TitleStatusHype
DPLib: A Standard Benchmark Library for Distributed Power System Analysis and OptimizationCode1
FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated LearningCode1
Federated Optimization in Heterogeneous NetworksCode1
GNN-Empowered Effective Partial Observation MARL Method for AoI Management in Multi-UAV NetworkCode1
Just One Byte (per gradient): A Note on Low-Bandwidth Decentralized Language Model Finetuning Using Shared RandomnessCode1
MANGO: A Python Library for Parallel Hyperparameter TuningCode1
Federated Learning as Variational Inference: A Scalable Expectation Propagation ApproachCode1
Optimization Algorithm Design via Electric CircuitsCode1
ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM TrainingCode1
SCAFFOLD: Stochastic Controlled Averaging for Federated LearningCode1
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