Optimal Data Splitting in Distributed Optimization for Machine Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Medyakov, Daniil, Molodtsov, Gleb, Beznosikov, Aleksandr, Gasnikov, Alexander |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Effective Method with Compression for Distributed and Federated Cocoercive Variational Inequalities
by: Medyakov, Daniil, et al.
Published: (2024)
by: Medyakov, Daniil, et al.
Published: (2024)
Shuffling Heuristic in Variational Inequalities: Establishing New Convergence Guarantees
by: Medyakov, Daniil, et al.
Published: (2025)
by: Medyakov, Daniil, et al.
Published: (2025)
Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency through Shuffling
by: Medyakov, Daniil, et al.
Published: (2025)
by: Medyakov, Daniil, et al.
Published: (2025)
Sign-SGD via Parameter-Free Optimization
by: Medyakov, Daniil, et al.
Published: (2025)
by: Medyakov, Daniil, et al.
Published: (2025)
Hierarchical Mixture-of-Experts with Two-Stage Optimization
by: Molodtsov, Gleb, et al.
Published: (2026)
by: Molodtsov, Gleb, et al.
Published: (2026)
Bant: Byzantine Antidote via Trial Function and Trust Scores
by: Molodtsov, Gleb, et al.
Published: (2025)
by: Molodtsov, Gleb, et al.
Published: (2025)
Distributed Saddle-Point Problems: Lower Bounds, Near-Optimal and Robust Algorithms
by: Beznosikov, Aleksandr, et al.
Published: (2020)
by: Beznosikov, Aleksandr, et al.
Published: (2020)
Gradient-Free Approaches is a Key to an Efficient Interaction with Markovian Stochasticity
by: Prokhorov, Boris, et al.
Published: (2026)
by: Prokhorov, Boris, et al.
Published: (2026)
Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities
by: Beznosikov, Aleksandr, et al.
Published: (2023)
by: Beznosikov, Aleksandr, et al.
Published: (2023)
Accelerated Methods with Compressed Communications for Distributed Optimization Problems under Data Similarity
by: Bylinkin, Dmitry, et al.
Published: (2024)
by: Bylinkin, Dmitry, et al.
Published: (2024)
First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities
by: Beznosikov, Aleksandr, et al.
Published: (2023)
by: Beznosikov, Aleksandr, et al.
Published: (2023)
Activations and Gradients Compression for Model-Parallel Training
by: Rudakov, Mikhail, et al.
Published: (2024)
by: Rudakov, Mikhail, et al.
Published: (2024)
Accelerated Stochastic ExtraGradient: Mixing Hessian and Gradient Similarity to Reduce Communication in Distributed and Federated Learning
by: Bylinkin, Dmitry, et al.
Published: (2024)
by: Bylinkin, Dmitry, et al.
Published: (2024)
Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting
by: Ustimenko, Aleksei, et al.
Published: (2023)
by: Ustimenko, Aleksei, et al.
Published: (2023)
Optimal Analysis of Method with Batching for Monotone Stochastic Finite-Sum Variational Inequalities
by: Pichugin, Alexander, et al.
Published: (2024)
by: Pichugin, Alexander, et al.
Published: (2024)
Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness
by: Kornilov, Nikita, et al.
Published: (2025)
by: Kornilov, Nikita, et al.
Published: (2025)
One-Point Feedback for Composite Optimization with Applications to Distributed and Federated Learning
by: Beznosikov, Aleksandr, et al.
Published: (2021)
by: Beznosikov, Aleksandr, et al.
Published: (2021)
Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features
by: Beznosikov, Aleksandr, et al.
Published: (2023)
by: Beznosikov, Aleksandr, et al.
Published: (2023)
Random-reshuffled SARAH does not need a full gradient computations
by: Beznosikov, Aleksandr, et al.
Published: (2021)
by: Beznosikov, Aleksandr, et al.
Published: (2021)
Exploring New Frontiers in Vertical Federated Learning: the Role of Saddle Point Reformulation
by: Beznosikov, Aleksandr, et al.
Published: (2026)
by: Beznosikov, Aleksandr, et al.
Published: (2026)
Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed
by: Chezhegov, Savelii, et al.
Published: (2024)
by: Chezhegov, Savelii, et al.
Published: (2024)
Communication-Efficient Federated Learning with Adaptive Number of Participants
by: Skorik, Sergey, et al.
Published: (2025)
by: Skorik, Sergey, et al.
Published: (2025)
Accelerated Methods with Compression for Horizontal and Vertical Federated Learning
by: Stanko, Sergey, et al.
Published: (2024)
by: Stanko, Sergey, et al.
Published: (2024)
Convergence of Clipped-SGD for Convex $(L_0,L_1)$-Smooth Optimization with Heavy-Tailed Noise
by: Chezhegov, Savelii, et al.
Published: (2025)
by: Chezhegov, Savelii, et al.
Published: (2025)
Decentralized Personalized Federated Learning for Min-Max Problems
by: Borodich, Ekaterina, et al.
Published: (2021)
by: Borodich, Ekaterina, et al.
Published: (2021)
Lower Bounds and Optimal Algorithms for Non-Smooth Convex Decentralized Optimization over Time-Varying Networks
by: Kovalev, Dmitry, et al.
Published: (2024)
by: Kovalev, Dmitry, et al.
Published: (2024)
On Biased Compression for Distributed Learning
by: Beznosikov, Aleksandr, et al.
Published: (2020)
by: Beznosikov, Aleksandr, et al.
Published: (2020)
Accelerated Methods with Complexity Separation Under Data Similarity for Federated Learning Problems
by: Bylinkin, Dmitry, et al.
Published: (2026)
by: Bylinkin, Dmitry, et al.
Published: (2026)
Where Does Warm-Up Come From? Adaptive Scheduling for Norm-Constrained Optimizers
by: Riabinin, Artem, et al.
Published: (2026)
by: Riabinin, Artem, et al.
Published: (2026)
Extragradient Sliding for Composite Non-Monotone Variational Inequalities
by: Emelyanov, Roman, et al.
Published: (2024)
by: Emelyanov, Roman, et al.
Published: (2024)
Local SGD for Near-Quadratic Problems: Improving Convergence under Unconstrained Noise Conditions
by: Sadchikov, Andrey, et al.
Published: (2024)
by: Sadchikov, Andrey, et al.
Published: (2024)
WeightLoRA: Keep Only Necessary Adapters
by: Veprikov, Andrey, et al.
Published: (2025)
by: Veprikov, Andrey, et al.
Published: (2025)
Enhancing Stability of Physics-Informed Neural Network Training Through Saddle-Point Reformulation
by: Bylinkin, Dmitry, et al.
Published: (2025)
by: Bylinkin, Dmitry, et al.
Published: (2025)
Zero-Order Optimization for LLM Fine-Tuning via Learnable Direction Sampling
by: Parfenov, Valery, et al.
Published: (2026)
by: Parfenov, Valery, et al.
Published: (2026)
Decentralized Distributed Optimization for Saddle Point Problems
by: Rogozin, Alexander, et al.
Published: (2021)
by: Rogozin, Alexander, et al.
Published: (2021)
Method with Batching for Stochastic Finite-Sum Variational Inequalities in Non-Euclidean Setting
by: Pichugin, Alexander, et al.
Published: (2024)
by: Pichugin, Alexander, et al.
Published: (2024)
Leveraging Coordinate Momentum in SignSGD and Muon: Memory-Optimized Zero-Order
by: Petrov, Egor, et al.
Published: (2025)
by: Petrov, Egor, et al.
Published: (2025)
Preconditioned Norms: A Unified Framework for Steepest Descent, Quasi-Newton and Adaptive Methods
by: Veprikov, Andrey, et al.
Published: (2025)
by: Veprikov, Andrey, et al.
Published: (2025)
Power of Generalized Smoothness in Stochastic Convex Optimization: First- and Zero-Order Algorithms
by: Lobanov, Aleksandr, et al.
Published: (2025)
by: Lobanov, Aleksandr, et al.
Published: (2025)
Decentralized Finite-Sum Optimization over Time-Varying Networks
by: Metelev, Dmitry, et al.
Published: (2024)
by: Metelev, Dmitry, et al.
Published: (2024)
Similar Items
-
Effective Method with Compression for Distributed and Federated Cocoercive Variational Inequalities
by: Medyakov, Daniil, et al.
Published: (2024) -
Shuffling Heuristic in Variational Inequalities: Establishing New Convergence Guarantees
by: Medyakov, Daniil, et al.
Published: (2025) -
Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency through Shuffling
by: Medyakov, Daniil, et al.
Published: (2025) -
Sign-SGD via Parameter-Free Optimization
by: Medyakov, Daniil, et al.
Published: (2025) -
Hierarchical Mixture-of-Experts with Two-Stage Optimization
by: Molodtsov, Gleb, et al.
Published: (2026)