Cover Image

Decentralized Nonconvex Optimization under Heavy-Tailed Noise: Normalization and Optimal Convergence

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yu, Shuhua, Jakovetic, Dusan, Kar, Soummya
Format:	Preprint
Published:	2025
Subjects:	Optimization and Control Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Smoothed Gradient Clipping and Error Feedback for Decentralized Optimization under Symmetric Heavy-Tailed Noise
by: Yu, Shuhua, et al.
Published: (2023)

Decentralized Nonsmooth Nonconvex Optimization with Client Sampling
by: Chen, Xinyan, et al.
Published: (2026)

Problem-Parameter-Free Decentralized Nonconvex Stochastic Optimization
by: Li, Jiaxiang, et al.
Published: (2024)

Optimality in Decentralized Optimization under Bandwidth Constraints
by: Tyurin, Alexander
Published: (2026)

Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization
by: Qin, Zhen, et al.
Published: (2023)

A Unified Framework for Center-based Clustering of Distributed Data
by: Armacki, Aleksandar, et al.
Published: (2024)

Decentralized Distributed Optimization for Saddle Point Problems
by: Rogozin, Alexander, et al.
Published: (2021)

Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentum
by: Zhou, Yuan, et al.
Published: (2025)

Decentralized Gradient-Free Methods for Stochastic Non-Smooth Non-Convex Optimization
by: Lin, Zhenwei, et al.
Published: (2023)

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

A Single-Loop Algorithm for Decentralized Bilevel Optimization
by: Dong, Youran, et al.
Published: (2023)

Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity
by: Mahran, Ammar, et al.
Published: (2026)

Communication-Efficient Federated Optimization over Semi-Decentralized Networks
by: Wang, He, et al.
Published: (2023)

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

A Stochastic Approximation Approach for Efficient Decentralized Optimization on Random Networks
by: Yau, Chung-Yiu, et al.
Published: (2024)

A Double Tracking Method for Optimization with Decentralized Generalized Orthogonality Constraints
by: Wang, Lei, et al.
Published: (2024)

Distributed Stochastic Momentum Tracking with Local Updates: Achieving Optimal Communication and Iteration Complexities
by: Huang, Kun, et al.
Published: (2025)

Large-Scale LLM Inference with Heterogeneous Workloads: Prefill-Decode Contention and Asymptotically Optimal Control
by: Lin, Ruihan, et al.
Published: (2026)

S$^3$LDBO: A Snapshot Single-Loop Algorithm for Decentralized Bilevel Optimization
by: Yin, Chao, et al.
Published: (2026)

Accelerating Optimal Power Flow with GPUs: SIMD Abstraction of Nonlinear Programs and Condensed-Space Interior-Point Methods
by: Shin, Sungho, et al.
Published: (2023)

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization
by: Huang, Yan, et al.
Published: (2025)

Tailoring Gradient Methods for Differentially-Private Distributed Optimization
by: Wang, Yongqiang, et al.
Published: (2022)

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

Distributed Constraint-Coupled Optimization: Harnessing ADMM-consensus for robustness
by: Messilem, Mohamed Abdelmouamin, et al.
Published: (2025)

Distributed Constrained Combinatorial Optimization leveraging Hypergraph Neural Networks
by: Heydaribeni, Nasimeh, et al.
Published: (2023)

One-Point Feedback for Composite Optimization with Applications to Distributed and Federated Learning
by: Beznosikov, Aleksandr, et al.
Published: (2021)

Non-ergodic linear convergence property of the delayed gradient descent under the strongly convexity and the Polyak-Łojasiewicz condition
by: Choi, Hyung Jun, et al.
Published: (2023)

The Privacy Power of Correlated Noise in Decentralized Learning
by: Allouah, Youssef, et al.
Published: (2024)

From Sequential to Parallel: Reformulating Dynamic Programming as GPU Kernels for Large-Scale Stochastic Combinatorial Optimization
by: Zhao, Jingyi, et al.
Published: (2026)

A Survey of Optimization Methods for Training DL Models: Theoretical Perspective on Convergence and Generalization
by: Wang, Jing, et al.
Published: (2025)

Decentralized Directed Collaboration for Personalized Federated Learning
by: Liu, Yingqi, et al.
Published: (2024)

CEDAS: A Compressed Decentralized Stochastic Gradient Method with Improved Convergence
by: Huang, Kun, et al.
Published: (2023)

Decentralized Personalized Federated Learning for Min-Max Problems
by: Borodich, Ekaterina, et al.
Published: (2021)

Energy-efficient Decentralized Learning via Graph Sparsification
by: Zhang, Xusheng, et al.
Published: (2024)

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

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

Sharp High-Probability Rates for Nonlinear SGD under Heavy-Tailed Noise via Symmetrization
by: Armacki, Aleksandar, et al.
Published: (2025)

A Bias-Correction Decentralized Stochastic Gradient Algorithm with Momentum Acceleration
by: Hu, Yuchen, et al.
Published: (2025)

Resource-Constrained Decentralized Federated Learning via Personalized Event-Triggering
by: Zehtabi, Shahryar, et al.
Published: (2022)

A Penalty-Based Method for Communication-Efficient Decentralized Bilevel Programming
by: Nazari, Parvin, et al.
Published: (2022)