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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 26–50 of 536 papers

TitleStatusHype
Optimization Algorithm Design via Electric CircuitsCode1
Acceleration of Federated Learning with Alleviated Forgetting in Local TrainingCode1
Federated Accelerated Stochastic Gradient DescentCode1
ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM TrainingCode1
Federated Learning as Variational Inference: A Scalable Expectation Propagation ApproachCode1
Asynchronous Local-SGD Training for Language ModelingCode1
Byzantine-Robust Learning on Heterogeneous Datasets via BucketingCode1
BAGUA: Scaling up Distributed Learning with System RelaxationsCode1
Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over NetworksCode0
FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation RetrievalCode0
A Distributed Quasi-Newton Algorithm for Primal and Dual Regularized Empirical Risk MinimizationCode0
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed OptimizationCode0
Dynamic communication topologies for distributed heuristics in energy system optimization algorithmsCode0
Error Feedback Shines when Features are RareCode0
GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational ComplexityCode0
Distributed Optimization, Averaging via ADMM, and Network TopologyCode0
Differentially Private Distributed Estimation and LearningCode0
Distributed Adversarial Training to Robustify Deep Neural Networks at ScaleCode0
Distributed optimization for nonrigid nano-tomographyCode0
Efficient Randomized Subspace Embeddings for Distributed Optimization under a Communication BudgetCode0
Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of MultipliersCode0
Adding vs. Averaging in Distributed Primal-Dual OptimizationCode0
Cooperative Tuning of Multi-Agent Optimal Control SystemsCode0
A Distributed Quasi-Newton Algorithm for Empirical Risk Minimization with Nonsmooth RegularizationCode0
PIM-Opt: Demystifying Distributed Optimization Algorithms on a Real-World Processing-In-Memory SystemCode0
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