Cover Image

Optimal Complexity in Byzantine-Robust Distributed Stochastic Optimization with Data Heterogeneity

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Shi, Qiankun, Peng, Jie, Yuan, Kun, Wang, Xiao, Ling, Qing
Format:	Preprint
Published:	2025
Subjects:	Optimization and Control Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Bregman Linearized Augmented Lagrangian Method for Nonconvex Constrained Stochastic Zeroth-order Optimization
by: Shi, Qiankun, et al.
Published: (2025)

Robust Out-of-Distribution Stochastic Optimization
by: Li, Xianyu, et al.
Published: (2026)

Optimal Rates for Robust Stochastic Convex Optimization
by: Gao, Changyu, et al.
Published: (2024)

Achieving Linear Speedup with ProxSkip in Distributed Stochastic Optimization
by: Guo, Luyao, et al.
Published: (2023)

Sinkhorn Distributionally Robust Optimization
by: Wang, Jie, et al.
Published: (2021)

Byzantine-Robust Distributed SGD: A Unified Analysis and Tight Error Bounds
by: Ruan, Boyuan, et al.
Published: (2026)

From Inexact Gradients to Byzantine Robustness: Acceleration and Optimization under Similarity
by: Gaucher, Renaud, et al.
Published: (2026)

Near-Optimal Algorithms for Group Distributionally Robust Optimization and Beyond
by: Soma, Tasuku, et al.
Published: (2022)

Unifying Distributionally Robust Optimization via Optimal Transport Theory
by: Blanchet, Jose, et al.
Published: (2023)

Unified Breakdown Analysis for Byzantine Robust Gossip
by: Gaucher, Renaud, et al.
Published: (2024)

Optimal Data Splitting in Distributed Optimization for Machine Learning
by: Medyakov, Daniil, et al.
Published: (2024)

Nested Stochastic Algorithm for Generalized Sinkhorn distance-Regularized Distributionally Robust Optimization
by: Yang, Yufeng, et al.
Published: (2025)

Wasserstein Distributionally Robust Nash Equilibrium Seeking with Heterogeneous Data: A Lagrangian Approach
by: Wang, Zifan, et al.
Published: (2025)

Lions and Muons: Optimization via Stochastic Frank-Wolfe
by: Sfyraki, Maria-Eleni, 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)

A Nearly Optimal Single Loop Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness
by: Gong, Xiaochuan, et al.
Published: (2024)

Decentralized Non-convex Stochastic Optimization with Heterogeneous Variance
by: Chen, Hongxu, et al.
Published: (2026)

Stochastic-Constrained Stochastic Optimization with Markovian Data
by: Kim, Yeongjong, et al.
Published: (2023)

Stochastic Approximation with Block Coordinate Optimal Stepsizes
by: Jiang, Tao, et al.
Published: (2025)

Achieving ${O}(ε^{-1.5})$ Complexity in Hessian/Jacobian-free Stochastic Bilevel Optimization
by: Yang, Yifan, et al.
Published: (2023)

Distributionally Robust Optimization
by: Kuhn, Daniel, et al.
Published: (2024)

Sample Complexity of Stochastic Optimization with Integer Variables
by: Cheng, Hongyu, et al.
Published: (2026)

Near-Optimal Decentralized Stochastic Nonconvex Optimization with Heavy-Tailed Noise
by: Wang, Menglian, et al.
Published: (2026)

Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions
by: Islamov, Rustem, et al.
Published: (2026)

Communication Efficient Federated Learning with Linear Convergence on Heterogeneous Data
by: Liu, Jie, et al.
Published: (2025)

From PowerSGD to PowerSGD+: Low-Rank Gradient Compression for Distributed Optimization with Convergence Guarantees
by: Xie, Shengping, et al.
Published: (2025)

Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walks
by: Hu, Jie, et al.
Published: (2024)

Freya PAGE: First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with Heterogeneous Asynchronous Computations
by: Tyurin, Alexander, et al.
Published: (2024)

Decentralized Bilevel Optimization: A Perspective from Transient Iteration Complexity
by: Kong, Boao, et al.
Published: (2024)

Sample Complexity of Distributionally Robust Average-Reward Reinforcement Learning
by: Chen, Zijun, et al.
Published: (2025)

The Sample Complexity of Parameter-Free Stochastic Convex Optimization
by: Lawrence, Jared, et al.
Published: (2025)

The Sample Complexity of Gradient Descent in Stochastic Convex Optimization
by: Livni, Roi
Published: (2024)

On the Complexity of First-Order Methods in Stochastic Bilevel Optimization
by: Kwon, Jeongyeol, et al.
Published: (2024)

Nonlinear Distributionally Robust Optimization
by: Sheriff, Mohammed Rayyan, et al.
Published: (2023)

Distributionally-Robust Learning to Optimize
by: Ranjan, Vinit, et al.
Published: (2026)

Sample Complexity of Variance-reduced Distributionally Robust Q-learning
by: Wang, Shengbo, et al.
Published: (2023)

Clapping: Removing Per-sample Storage for Pipeline Parallel Distributed Optimization with Communication Compression
by: Kong, Boao, et al.
Published: (2025)

Compressed Proximal Federated Learning for Non-Convex Composite Optimization on Heterogeneous Data
by: Qiu, Pu, et al.
Published: (2026)

On the Convergence of Stochastic Gradient Descent with Perturbed Forward-Backward Passes
by: Kong, Boao, et al.
Published: (2026)

On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization
by: Liang, Ling, et al.
Published: (2024)