Communication-Efficient Federated Learning with Adaptive Number of Participants
Fuente:
arXiv
Saved in:
| Main Authors: | Skorik, Sergey, Dorofeev, Vladislav, Molodtsov, Gleb, Avetisyan, Aram, Bylinkin, Dmitry, Medyakov, Daniil, Beznosikov, Aleksandr |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
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)
Bant: Byzantine Antidote via Trial Function and Trust Scores
by: Molodtsov, Gleb, et al.
Published: (2025)
by: Molodtsov, Gleb, et al.
Published: (2025)
Optimal Data Splitting in Distributed Optimization for Machine Learning
by: Medyakov, Daniil, et al.
Published: (2024)
by: Medyakov, Daniil, et al.
Published: (2024)
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)
SSSD-ECG-nle: New Label Embeddings with Structured State-Space Models for ECG generation
by: Skorik, Sergey, et al.
Published: (2024)
by: Skorik, Sergey, et al.
Published: (2024)
Scalable Knowledge Editing for Mixture-of-Experts LLMs via Tensor-Structured Updates
by: Maksimov, Roman, et al.
Published: (2026)
by: Maksimov, Roman, et al.
Published: (2026)
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)
Sign-SGD via Parameter-Free Optimization
by: Medyakov, Daniil, et al.
Published: (2025)
by: Medyakov, Daniil, et al.
Published: (2025)
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)
HARP: Hadamard-Preconditioned Adaptive Rotation Processor for Extreme LLM Quantization
by: Zagitov, Artur, et al.
Published: (2026)
by: Zagitov, Artur, et al.
Published: (2026)
Local Methods with Adaptivity via Scaling
by: Chezhegov, Savelii, et al.
Published: (2024)
by: Chezhegov, Savelii, et al.
Published: (2024)
Hierarchical Mixture-of-Experts with Two-Stage Optimization
by: Molodtsov, Gleb, et al.
Published: (2026)
by: Molodtsov, Gleb, et al.
Published: (2026)
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)
Extreme Low-Bit Inference in Reasoning Models: Failure Modes and Targeted Recovery
by: Alimaskina, Ekaterina, et al.
Published: (2026)
by: Alimaskina, Ekaterina, et al.
Published: (2026)
Bregman Proximal Method for Efficient Communications under Similarity
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)
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)
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)
FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training
by: Zmushko, Philip, et al.
Published: (2024)
by: Zmushko, Philip, et al.
Published: (2024)
Just a Simple Transformation is Enough for Data Protection in Vertical Federated Learning
by: Semenov, Andrei, et al.
Published: (2024)
by: Semenov, Andrei, et al.
Published: (2024)
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)
Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents
by: Labbi, Safwan, et al.
Published: (2024)
by: Labbi, Safwan, et al.
Published: (2024)
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)
Label Privacy in Split Learning for Large Models with Parameter-Efficient Training
by: Zmushko, Philip, et al.
Published: (2024)
by: Zmushko, Philip, et al.
Published: (2024)
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)
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)
Self-Trained Model for ECG Complex Delineation
by: Avetisyan, Aram, et al.
Published: (2024)
by: Avetisyan, Aram, et al.
Published: (2024)
On Biased Compression for Distributed Learning
by: Beznosikov, Aleksandr, et al.
Published: (2020)
by: Beznosikov, Aleksandr, et al.
Published: (2020)
Communication Efficient Adaptive Model-Driven Quantum Federated Learning
by: Gurung, Dev, et al.
Published: (2025)
by: Gurung, Dev, 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)
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)
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)
Stochastic Gradient Methods with Preconditioned Updates
by: Sadiev, Abdurakhmon, et al.
Published: (2022)
by: Sadiev, Abdurakhmon, et al.
Published: (2022)
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)
LionMuon: Alternating Spectral and Sign Descent for Efficient Training
by: Bolatov, Arman, et al.
Published: (2026)
by: Bolatov, Arman, et al.
Published: (2026)
An Adaptive Clustering Scheme for Client Selections in Communication-Efficient Federated Learning
by: Chen, Yan-Ann, et al.
Published: (2025)
by: Chen, Yan-Ann, et al.
Published: (2025)
Adaptive Destruction Processes for Diffusion Samplers
by: Gritsaev, Timofei, et al.
Published: (2025)
by: Gritsaev, Timofei, et al.
Published: (2025)
Softsign: Smooth Sign in Your Optimizer For Better Parameter Heterogeneity Handling
by: Feoktistov, Dmitrii, et al.
Published: (2026)
by: Feoktistov, Dmitrii, et al.
Published: (2026)
Similar Items
-
Accelerated Methods with Complexity Separation Under Data Similarity for Federated Learning Problems
by: Bylinkin, Dmitry, et al.
Published: (2026) -
Bant: Byzantine Antidote via Trial Function and Trust Scores
by: Molodtsov, Gleb, et al.
Published: (2025) -
Optimal Data Splitting in Distributed Optimization for Machine Learning
by: Medyakov, Daniil, et al.
Published: (2024) -
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)