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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
A Distributed Quasi-Newton Algorithm for Primal and Dual Regularized Empirical Risk MinimizationCode0
A primal-dual perspective for distributed TD-learningCode0
Byzantine-Robust Loopless Stochastic Variance-Reduced GradientCode0
Transmission Investment Coordination using MILP Lagrange Dual Decomposition and Auxiliary Problem PrincipleCode0
Manifold Identification for Ultimately Communication-Efficient Distributed OptimizationCode0
On the Convergence of Decentralized Federated Learning Under Imperfect Information SharingCode0
Adding vs. Averaging in Distributed Primal-Dual OptimizationCode0
GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational ComplexityCode0
L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual FrameworkCode0
Federated Learning with Compression: Unified Analysis and Sharp GuaranteesCode0
An Accelerated Communication-Efficient Primal-Dual Optimization Framework for Structured Machine LearningCode0
Accelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUsCode0
Local SGD with Periodic Averaging: Tighter Analysis and Adaptive SynchronizationCode0
Private Multi-Task Learning: Formulation and Applications to Federated LearningCode0
Error Feedback Shines when Features are RareCode0
Dynamic communication topologies for distributed heuristics in energy system optimization algorithmsCode0
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed OptimizationCode0
Distributed Optimization using Heterogeneous Compute SystemsCode0
Distributed Optimization, Averaging via ADMM, and Network TopologyCode0
Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of MultipliersCode0
Distributed optimization for nonrigid nano-tomographyCode0
Differentially Private Distributed Estimation and LearningCode0
DASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity, and No Client SynchronizationCode0
Distributed Optimization with Arbitrary Local SolversCode0
Cooperative Tuning of Multi-Agent Optimal Control SystemsCode0
Distributed Adversarial Training to Robustify Deep Neural Networks at ScaleCode0
Communication Efficient Distributed Optimization using an Approximate Newton-type MethodCode0
FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation RetrievalCode0
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural NetworksCode0
Communication- and Computation-Efficient Distributed Submodular Optimization in Robot Mesh NetworksCode0
Federated Learning: Challenges, Methods, and Future DirectionsCode0
PIM-Opt: Demystifying Distributed Optimization Algorithms on a Real-World Processing-In-Memory SystemCode0
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved RatesCode0
Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation AnalysisCode0
Efficient Randomized Subspace Embeddings for Distributed Optimization under a Communication BudgetCode0
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local ComputationsCode0
Asymptotic Network Independence in Distributed Stochastic Optimization for Machine Learning—0
A survey on secure decentralized optimization and learning—0
A Federated Distributionally Robust Support Vector Machine with Mixture of Wasserstein Balls Ambiguity Set for Distributed Fault Diagnosis—0
A Survey on Distributed Evolutionary Computation—0
A Survey of Resilient Coordination for Cyber-Physical Systems Against Malicious Attacks—0
Advances in Asynchronous Parallel and Distributed Optimization—0
A Survey of Optimization Methods for Training DL Models: Theoretical Perspective on Convergence and Generalization—0
A Stochastic Large-scale Machine Learning Algorithm for Distributed Features and Observations—0
A Sequential Approximation Framework for Coded Distributed Optimization—0
A Semi-Distributed Interior Point Algorithm for Optimal Coordination of Automated Vehicles at Intersections—0
A Dual Approach for Optimal Algorithms in Distributed Optimization over Networks—0
ADMM for Downlink Beamforming in Cell-Free Massive MIMO Systems—0
A Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flow—0
A Provably Communication-Efficient Asynchronous Distributed Inference Method for Convex and Nonconvex Problems—0
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