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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
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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