Image de couverture de livre

Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Beznosikov, Aleksandr, Takáč, Martin, Gasnikov, Alexander
Format:	Preprint
Publié:	2023
Sujets:	Optimization and Control Distributed, Parallel, and Cluster Computing Computer Science and Game Theory Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

One-Point Feedback for Composite Optimization with Applications to Distributed and Federated Learning
par: Beznosikov, Aleksandr, et autres
Publié: (2021)

Accelerated Methods with Compressed Communications for Distributed Optimization Problems under Data Similarity
par: Bylinkin, Dmitry, et autres
Publié: (2024)

Distributed Saddle-Point Problems: Lower Bounds, Near-Optimal and Robust Algorithms
par: Beznosikov, Aleksandr, et autres
Publié: (2020)

Activations and Gradients Compression for Model-Parallel Training
par: Rudakov, Mikhail, et autres
Publié: (2024)

Decentralized Distributed Optimization for Saddle Point Problems
par: Rogozin, Alexander, et autres
Publié: (2021)

Decentralized Personalized Federated Learning for Min-Max Problems
par: Borodich, Ekaterina, et autres
Publié: (2021)

On Biased Compression for Distributed Learning
par: Beznosikov, Aleksandr, et autres
Publié: (2020)

Local Methods with Adaptivity via Scaling
par: Chezhegov, Savelii, et autres
Publié: (2024)

Average-case optimization analysis for distributed consensus algorithms on regular graphs
par: Nguyen, Nhat Trung, et autres
Publié: (2024)

LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression
par: Condat, Laurent, et autres
Publié: (2024)

Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis
par: Luo, Ruichen, et autres
Publié: (2025)

Designing Local Distributed Mechanisms
par: Hirvonen, Juho, et autres
Publié: (2024)

Distributed Stochastic Momentum Tracking with Local Updates: Achieving Optimal Communication and Iteration Complexities
par: Huang, Kun, et autres
Publié: (2025)

Accelerating Distributed Optimization: A Primal-Dual Perspective on Local Steps
par: Yang, Junchi, et autres
Publié: (2024)

Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression
par: He, Yutong, et autres
Publié: (2023)

Unbiased Compression Saves Communication in Distributed Optimization: When and How Much?
par: He, Yutong, et autres
Publié: (2023)

Distributed Agreement in the Arrovian Framework
par: Wood, Kenan, et autres
Publié: (2024)

Distributed MIS Algorithms for Rational Agents using Games
par: Salevemula, Nithin, et autres
Publié: (2025)

BROADCAST: Reducing Both Stochastic and Compression Noise to Robustify Communication-Efficient Federated Learning
par: Zhu, Heng, et autres
Publié: (2021)

The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication
par: Patel, Kumar Kshitij, et autres
Publié: (2024)

A GPU-Accelerated Distributed Algorithm for Optimal Power Flow in Distribution Systems
par: Ryu, Minseok, et autres
Publié: (2025)

Distributed Variational Quantum Linear Solver
par: Shen, Tong, et autres
Publié: (2026)

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization
par: Huang, Yan, et autres
Publié: (2025)

Tailoring Gradient Methods for Differentially-Private Distributed Optimization
par: Wang, Yongqiang, et autres
Publié: (2022)

Communication-Efficient Federated Bilevel Optimization with Local and Global Lower Level Problems
par: Li, Junyi, et autres
Publié: (2023)

Asynchronous Policy Gradient Aggregation for Efficient Distributed Reinforcement Learning
par: Tyurin, Alexander, et autres
Publié: (2025)

Load Balancing with Network Latencies via Distributed Gradient Descent
par: Balseiro, Santiago R., et autres
Publié: (2025)

Distributed Constraint-Coupled Optimization: Harnessing ADMM-consensus for robustness
par: Messilem, Mohamed Abdelmouamin, et autres
Publié: (2025)

D-PDLP: Scaling PDLP to Distributed Multi-GPU Systems
par: Li, Hongpei, et autres
Publié: (2026)

Distributed Constrained Combinatorial Optimization leveraging Hypergraph Neural Networks
par: Heydaribeni, Nasimeh, et autres
Publié: (2023)

Computing the Schulze Method for Large-Scale Preference Data Sets
par: Csar, Theresa, et autres
Publié: (2025)

Stochastic Controlled Averaging for Federated Learning with Communication Compression
par: Huang, Xinmeng, et autres
Publié: (2023)

Modeling Task Mapping for Data-intensive Applications in Heterogeneous Systems
par: Wilhelm, Martin, et autres
Publié: (2022)

Uncoded Storage Coded Transmission Elastic Computing with Straggler Tolerance in Heterogeneous Systems
par: Zhong, Xi, et autres
Publié: (2024)

Optimal Allocation of Tasks and Price of Anarchy of Distributed Optimization in Networked Computing Facilities
par: Mancuso, Vincenzo, et autres
Publié: (2024)

Efficient Gradient Methods for Distributed Saddle Problems
par: Luo, Ruichen, et autres
Publié: (2026)

Communication Efficient Distributed Training with Distributed Lion
par: Liu, Bo, et autres
Publié: (2024)

Effective Two-Stage Double Auction for Dynamic Resource Provision over Edge Networks via Discovering The Power of Overbooking
par: Wu, Sicheng, et autres
Publié: (2025)

A mechanism design overview of Sedna
par: Marsh, Benjamin, et autres
Publié: (2026)

A Management Framework for Vehicular Cloudtoward Economic and Environmental Efficiency
par: Patanè, Rosario, et autres
Publié: (2025)