Cover Image

Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Mahran, Ammar, Maranjyan, Artavazd, Richtárik, Peter
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Distributed, Parallel, and Cluster Computing Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Ringleader ASGD: The First Asynchronous SGD with Optimal Time Complexity under Data Heterogeneity
by: Maranjyan, Artavazd, et al.
Published: (2025)

LOSCAR-SGD: Local SGD with Communication-Computation Overlap and Delay-Corrected Sparse Model Averaging
by: Maziane, Yassine, et al.
Published: (2026)

First Provably Optimal Asynchronous SGD for Homogeneous and Heterogeneous Data
by: Maranjyan, Artavazd
Published: (2026)

Ringmaster ASGD: The First Asynchronous SGD with Optimal Time Complexity
by: Maranjyan, Artavazd, et al.
Published: (2025)

MindFlayer SGD: Efficient Parallel SGD in the Presence of Heterogeneous and Random Worker Compute Times
by: Maranjyan, Artavazd, et al.
Published: (2024)

LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression
by: Condat, Laurent, et al.
Published: (2024)

Ringmaster LMO: Asynchronous Linear Minimization Oracle Momentum Method
by: Sadiev, Abdurakhmon, et al.
Published: (2026)

Rennala MVR: Improved Time Complexity for Parallel Stochastic Optimization via Momentum-Based Variance Reduction
by: Tovmasyan, Zhirayr, et al.
Published: (2026)

GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity
by: Maranjyan, Artavazd, et al.
Published: (2022)

ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning
by: Maranjyan, Artavazd, et al.
Published: (2025)

Birch SGD: A Tree Graph Framework for Local and Asynchronous SGD Methods
by: Tyurin, Alexander, et al.
Published: (2025)

The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication
by: Patel, Kumar Kshitij, et al.
Published: (2024)

On Biased Compression for Distributed Learning
by: Beznosikov, Aleksandr, et al.
Published: (2020)

Towards a Better Theoretical Understanding of Independent Subnetwork Training
by: Shulgin, Egor, et al.
Published: (2023)

Smoothed Normalization for Efficient Distributed Private Optimization
by: Shulgin, Egor, et al.
Published: (2025)

Correlated Quantization for Faster Nonconvex Distributed Optimization
by: Panferov, Andrei, et al.
Published: (2024)

Accelerated Methods with Compressed Communications for Distributed Optimization Problems under Data Similarity
by: Bylinkin, Dmitry, et al.
Published: (2024)

Asynchronous Policy Gradient Aggregation for Efficient Distributed Reinforcement Learning
by: Tyurin, Alexander, et al.
Published: (2025)

Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes
by: Huang, Yan, et al.
Published: (2024)

Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis
by: Luo, Ruichen, et al.
Published: (2025)

Distributed Saddle-Point Problems: Lower Bounds, Near-Optimal and Robust Algorithms
by: Beznosikov, Aleksandr, et al.
Published: (2020)

Optimizing Stochastic Gradient Push under Broadcast Communications
by: Nguyen, Tuan, et al.
Published: (2026)

MAST: Model-Agnostic Sparsified Training
by: Demidovich, Yury, et al.
Published: (2023)

Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences
by: Malinovsky, Grigory, et al.
Published: (2023)

FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Data
by: Zhou, Yuan, et al.
Published: (2025)

FADAS: Towards Federated Adaptive Asynchronous Optimization
by: Wang, Yujia, et al.
Published: (2024)

Accelerating Distributed Optimization: A Primal-Dual Perspective on Local Steps
by: Yang, Junchi, et al.
Published: (2024)

Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression
by: He, Yutong, et al.
Published: (2023)

Unbiased Compression Saves Communication in Distributed Optimization: When and How Much?
by: He, Yutong, et al.
Published: (2023)

Proving the Limited Scalability of Centralized Distributed Optimization via a New Lower Bound Construction
by: Tyurin, Alexander
Published: (2025)

A Hybrid Stochastic Gradient Tracking Method for Distributed Online Optimization Over Time-Varying Directed Networks
by: Shi, Xinli, et al.
Published: (2025)

A New Theoretical Perspective on Data Heterogeneity in Federated Optimization
by: Wang, Jiayi, et al.
Published: (2024)

Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability
by: Xiang, Ming, et al.
Published: (2024)

FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction
by: Wu, Feijie, et al.
Published: (2024)

Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGD
by: Wang, Jiayi, et al.
Published: (2020)

Online Distributed Learning with Quantized Finite-Time Coordination
by: Bastianello, Nicola, et al.
Published: (2023)

Efficient Adaptive Federated Optimization
by: Lee, Su Hyeong, et al.
Published: (2024)

A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion
by: Hanzely, Filip, et al.
Published: (2019)

Do We Need Asynchronous SGD? On the Near-Optimality of Synchronous Methods
by: Begunov, Grigory, et al.
Published: (2026)

A GPU-Accelerated Distributed Algorithm for Optimal Power Flow in Distribution Systems
by: Ryu, Minseok, et al.
Published: (2025)