Ringmaster ASGD: The First Asynchronous SGD with Optimal Time Complexity
Fuente:
arXiv
Saved in:
| Main Authors: | Maranjyan, Artavazd, Tyurin, Alexander, Richtárik, Peter |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| 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)
by: Maranjyan, Artavazd, et al.
Published: (2025)
Ringmaster LMO: Asynchronous Linear Minimization Oracle Momentum Method
by: Sadiev, Abdurakhmon, et al.
Published: (2026)
by: Sadiev, Abdurakhmon, et al.
Published: (2026)
First Provably Optimal Asynchronous SGD for Homogeneous and Heterogeneous Data
by: Maranjyan, Artavazd
Published: (2026)
by: Maranjyan, Artavazd
Published: (2026)
Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity
by: Mahran, Ammar, et al.
Published: (2026)
by: Mahran, Ammar, et al.
Published: (2026)
MindFlayer SGD: Efficient Parallel SGD in the Presence of Heterogeneous and Random Worker Compute Times
by: Maranjyan, Artavazd, et al.
Published: (2024)
by: Maranjyan, Artavazd, et al.
Published: (2024)
LOSCAR-SGD: Local SGD with Communication-Computation Overlap and Delay-Corrected Sparse Model Averaging
by: Maziane, Yassine, et al.
Published: (2026)
by: Maziane, Yassine, 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)
by: Tovmasyan, Zhirayr, et al.
Published: (2026)
GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity
by: Maranjyan, Artavazd, et al.
Published: (2022)
by: Maranjyan, Artavazd, et al.
Published: (2022)
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression
by: Condat, Laurent, et al.
Published: (2024)
by: Condat, Laurent, et al.
Published: (2024)
ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning
by: Maranjyan, Artavazd, et al.
Published: (2025)
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)
by: Tyurin, Alexander, et al.
Published: (2025)
Asynchronous Policy Gradient Aggregation for Efficient Distributed Reinforcement Learning
by: Tyurin, Alexander, et al.
Published: (2025)
by: Tyurin, Alexander, et al.
Published: (2025)
Do We Need Asynchronous SGD? On the Near-Optimality of Synchronous Methods
by: Begunov, Grigory, et al.
Published: (2026)
by: Begunov, Grigory, et al.
Published: (2026)
Proving the Limited Scalability of Centralized Distributed Optimization via a New Lower Bound Construction
by: Tyurin, Alexander
Published: (2025)
by: Tyurin, Alexander
Published: (2025)
Optimality in Decentralized Optimization under Bandwidth Constraints
by: Tyurin, Alexander
Published: (2026)
by: Tyurin, Alexander
Published: (2026)
Towards a Better Theoretical Understanding of Independent Subnetwork Training
by: Shulgin, Egor, et al.
Published: (2023)
by: Shulgin, Egor, et al.
Published: (2023)
On Biased Compression for Distributed Learning
by: Beznosikov, Aleksandr, et al.
Published: (2020)
by: Beznosikov, Aleksandr, et al.
Published: (2020)
Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis
by: Luo, Ruichen, et al.
Published: (2025)
by: Luo, Ruichen, 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)
by: Patel, Kumar Kshitij, et al.
Published: (2024)
Smoothed Normalization for Efficient Distributed Private Optimization
by: Shulgin, Egor, et al.
Published: (2025)
by: Shulgin, Egor, et al.
Published: (2025)
MAST: Model-Agnostic Sparsified Training
by: Demidovich, Yury, et al.
Published: (2023)
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)
by: Malinovsky, Grigory, et al.
Published: (2023)
Correlated Quantization for Faster Nonconvex Distributed Optimization
by: Panferov, Andrei, et al.
Published: (2024)
by: Panferov, Andrei, et al.
Published: (2024)
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)
Convergence Analysis of Decentralized ASGD
by: Tosi, Mauro DL, et al.
Published: (2023)
by: Tosi, Mauro DL, et al.
Published: (2023)
Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes
by: Huang, Yan, et al.
Published: (2024)
by: Huang, Yan, et al.
Published: (2024)
Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGD
by: Wang, Jiayi, et al.
Published: (2020)
by: Wang, Jiayi, et al.
Published: (2020)
A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion
by: Hanzely, Filip, et al.
Published: (2019)
by: Hanzely, Filip, et al.
Published: (2019)
Continuous-Time Analysis of Federated Averaging
by: Overman, Tom, et al.
Published: (2025)
by: Overman, Tom, et al.
Published: (2025)
Online Distributed Learning with Quantized Finite-Time Coordination
by: Bastianello, Nicola, et al.
Published: (2023)
by: Bastianello, Nicola, et al.
Published: (2023)
FADAS: Towards Federated Adaptive Asynchronous Optimization
by: Wang, Yujia, et al.
Published: (2024)
by: Wang, Yujia, et al.
Published: (2024)
Time-varying Mixing Matrix Design for Energy-efficient Decentralized Federated Learning
by: Zhang, Xusheng, et al.
Published: (2025)
by: Zhang, Xusheng, et al.
Published: (2025)
Shadowheart SGD: Distributed Asynchronous SGD with Optimal Time Complexity Under Arbitrary Computation and Communication Heterogeneity
by: Tyurin, Alexander, et al.
Published: (2024)
by: Tyurin, Alexander, et al.
Published: (2024)
Activations and Gradients Compression for Model-Parallel Training
by: Rudakov, Mikhail, et al.
Published: (2024)
by: Rudakov, Mikhail, et al.
Published: (2024)
A Hybrid Stochastic Gradient Tracking Method for Distributed Online Optimization Over Time-Varying Directed Networks
by: Shi, Xinli, et al.
Published: (2025)
by: Shi, Xinli, 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)
Ordered Momentum for Asynchronous SGD
by: Shi, Chang-Wei, et al.
Published: (2024)
by: Shi, Chang-Wei, et al.
Published: (2024)
Distributed Stochastic Momentum Tracking with Local Updates: Achieving Optimal Communication and Iteration Complexities
by: Huang, Kun, et al.
Published: (2025)
by: Huang, Kun, et al.
Published: (2025)
FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Data
by: Zhou, Yuan, et al.
Published: (2025)
by: Zhou, Yuan, et al.
Published: (2025)
Tight analyses of first-order methods with error feedback
by: Thomsen, Daniel Berg, et al.
Published: (2025)
by: Thomsen, Daniel Berg, et al.
Published: (2025)
Similar Items
-
Ringleader ASGD: The First Asynchronous SGD with Optimal Time Complexity under Data Heterogeneity
by: Maranjyan, Artavazd, et al.
Published: (2025) -
Ringmaster LMO: Asynchronous Linear Minimization Oracle Momentum Method
by: Sadiev, Abdurakhmon, et al.
Published: (2026) -
First Provably Optimal Asynchronous SGD for Homogeneous and Heterogeneous Data
by: Maranjyan, Artavazd
Published: (2026) -
Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity
by: Mahran, Ammar, et al.
Published: (2026) -
MindFlayer SGD: Efficient Parallel SGD in the Presence of Heterogeneous and Random Worker Compute Times
by: Maranjyan, Artavazd, et al.
Published: (2024)