Distributed Gradient Clustering: Convergence and the Effect of Initialization
Fuente:
arXiv
Guardado en:
| Autores principales: | Armacki, Aleksandar, Sharma, Himkant, Bajović, Dragana, Jakovetić, Dušan, Chakraborty, Mrityunjoy, Kar, Soummya |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
A Unified Framework for Center-based Clustering of Distributed Data
por: Armacki, Aleksandar, et al.
Publicado: (2024)
por: Armacki, Aleksandar, et al.
Publicado: (2024)
Sharp High-Probability Rates for Nonlinear SGD under Heavy-Tailed Noise via Symmetrization
por: Armacki, Aleksandar, et al.
Publicado: (2025)
por: Armacki, Aleksandar, et al.
Publicado: (2025)
High-probability Convergence Bounds for Nonlinear Stochastic Gradient Descent Under Heavy-tailed Noise
por: Armacki, Aleksandar, et al.
Publicado: (2023)
por: Armacki, Aleksandar, et al.
Publicado: (2023)
Large Deviation Upper Bounds and Improved MSE Rates of Nonlinear SGD: Heavy-tailed Noise and Power of Symmetry
por: Armacki, Aleksandar, et al.
Publicado: (2024)
por: Armacki, Aleksandar, et al.
Publicado: (2024)
Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization
por: Armacki, Aleksandar, et al.
Publicado: (2026)
por: Armacki, Aleksandar, et al.
Publicado: (2026)
Nonlinear Stochastic Gradient Descent and Heavy-tailed Noise: A Unified Framework and High-probability Guarantees
por: Armacki, Aleksandar, et al.
Publicado: (2024)
por: Armacki, Aleksandar, et al.
Publicado: (2024)
Distributed gradient methods under heavy-tailed communication noise
por: Vukovic, Manojlo, et al.
Publicado: (2025)
por: Vukovic, Manojlo, et al.
Publicado: (2025)
Tackling heavy-tailed noise in distributed estimation: Asymptotic performance and tradeoffs
por: Bajovic, Dragana, et al.
Publicado: (2026)
por: Bajovic, Dragana, et al.
Publicado: (2026)
Decentralized Nonconvex Optimization under Heavy-Tailed Noise: Normalization and Optimal Convergence
por: Yu, Shuhua, et al.
Publicado: (2025)
por: Yu, Shuhua, et al.
Publicado: (2025)
Smoothed Gradient Clipping and Error Feedback for Decentralized Optimization under Symmetric Heavy-Tailed Noise
por: Yu, Shuhua, et al.
Publicado: (2023)
por: Yu, Shuhua, et al.
Publicado: (2023)
High-Probability Convergence in Decentralized Stochastic Optimization with Gradient Tracking
por: Armacki, Aleksandar, et al.
Publicado: (2026)
por: Armacki, Aleksandar, et al.
Publicado: (2026)
High-Probability Convergence Guarantees of Decentralized SGD
por: Armacki, Aleksandar, et al.
Publicado: (2025)
por: Armacki, Aleksandar, et al.
Publicado: (2025)
Robust stochastic first order methods in heavy-tailed noise via medoid mini-batch gradient sampling
por: Vukovic, Manojlo, et al.
Publicado: (2026)
por: Vukovic, Manojlo, et al.
Publicado: (2026)
Robust Federated Learning under Adversarial Attacks via Loss-Based Client Clustering
por: Kritharakis, Emmanouil, et al.
Publicado: (2025)
por: Kritharakis, Emmanouil, et al.
Publicado: (2025)
Modular Federated Learning: A Meta-Framework Perspective
por: Vicente, Frederico, et al.
Publicado: (2025)
por: Vicente, Frederico, et al.
Publicado: (2025)
FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning
por: Kritharakis, Emmanouil, et al.
Publicado: (2025)
por: Kritharakis, Emmanouil, et al.
Publicado: (2025)
Model-Free Learning and Optimal Policy Design in Multi-Agent MDPs Under Probabilistic Agent Dropout
por: Fiscko, Carmel, et al.
Publicado: (2023)
por: Fiscko, Carmel, et al.
Publicado: (2023)
Distributed Sign Momentum with Local Steps for Training Transformers
por: Yu, Shuhua, et al.
Publicado: (2024)
por: Yu, Shuhua, et al.
Publicado: (2024)
Federated Multi-Objective Learning with Controlled Pareto Frontiers
por: Rao, Jiansheng, et al.
Publicado: (2025)
por: Rao, Jiansheng, et al.
Publicado: (2025)
Convergence of Gradient Descent with Small Initialization for Unregularized Matrix Completion
por: Ma, Jianhao, et al.
Publicado: (2024)
por: Ma, Jianhao, et al.
Publicado: (2024)
Improving the Convergence of Private Shuffled Gradient Methods with Public Data
por: Jiang, Shuli, et al.
Publicado: (2025)
por: Jiang, Shuli, et al.
Publicado: (2025)
Test-Time Scaling in Diffusion LLMs via Hidden Semi-Autoregressive Experts
por: Lee, Jihoon, et al.
Publicado: (2025)
por: Lee, Jihoon, et al.
Publicado: (2025)
Limit Theorems for Stochastic Gradient Descent with Infinite Variance
por: Blanchet, Jose, et al.
Publicado: (2024)
por: Blanchet, Jose, et al.
Publicado: (2024)
On the Convergence of Multicalibration Gradient Boosting
por: Haimovich, Daniel, et al.
Publicado: (2026)
por: Haimovich, Daniel, et al.
Publicado: (2026)
GCSAM: Gradient Centralized Sharpness Aware Minimization
por: Hassan, Mohamed, et al.
Publicado: (2025)
por: Hassan, Mohamed, et al.
Publicado: (2025)
On the Gradient Complexity of Private Optimization with Private Oracles
por: Menart, Michael, et al.
Publicado: (2025)
por: Menart, Michael, et al.
Publicado: (2025)
Greedy Low-Rank Gradient Compression for Distributed Learning with Convergence Guarantees
por: Chen, Chuyan, et al.
Publicado: (2025)
por: Chen, Chuyan, et al.
Publicado: (2025)
OmniLearn: A Framework for Distributed Deep Learning over Heterogeneous Clusters
por: Tyagi, Sahil, et al.
Publicado: (2025)
por: Tyagi, Sahil, et al.
Publicado: (2025)
Distributed Inexact Newton Method with Adaptive Step Sizes
por: Jakovetic, Dusan, et al.
Publicado: (2023)
por: Jakovetic, Dusan, et al.
Publicado: (2023)
Gradient Manipulation in Distributed Stochastic Gradient Descent with Strategic Agents: Truthful Incentives with Convergence Guarantees
por: Chen, Ziqin, et al.
Publicado: (2026)
por: Chen, Ziqin, et al.
Publicado: (2026)
GANs as Gradient Flows that Converge
por: Huang, Yu-Jui, et al.
Publicado: (2022)
por: Huang, Yu-Jui, et al.
Publicado: (2022)
On the Convergence Rate of LoRA Gradient Descent
por: Mu, Siqiao, et al.
Publicado: (2025)
por: Mu, Siqiao, et al.
Publicado: (2025)
Scalable Random Wavelet Features: Efficient Non-Stationary Kernel Approximation with Convergence Guarantees
por: Kumar, Sawan, et al.
Publicado: (2026)
por: Kumar, Sawan, et al.
Publicado: (2026)
Accelerated Gradient Descent for Faster Convergence with Minimal Overhead
por: Graca, Manuel, et al.
Publicado: (2026)
por: Graca, Manuel, et al.
Publicado: (2026)
Reusing Trajectories in Policy Gradients Enables Fast Convergence
por: Montenegro, Alessandro, et al.
Publicado: (2025)
por: Montenegro, Alessandro, et al.
Publicado: (2025)
Policy Gradient in Robust MDPs with Global Convergence Guarantee
por: Wang, Qiuhao, et al.
Publicado: (2022)
por: Wang, Qiuhao, et al.
Publicado: (2022)
On the Second-Order Convergence of Biased Policy Gradient Algorithms
por: Mu, Siqiao, et al.
Publicado: (2023)
por: Mu, Siqiao, et al.
Publicado: (2023)
Policy Gradient Methods for Risk-Sensitive Distributional Reinforcement Learning with Provable Convergence
por: Xiao, Minheng, et al.
Publicado: (2024)
por: Xiao, Minheng, et al.
Publicado: (2024)
Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization
por: Hoang, Bao, et al.
Publicado: (2024)
por: Hoang, Bao, et al.
Publicado: (2024)
Beyond Exact Gradients: Convergence of Stochastic Soft-Max Policy Gradient Methods with Entropy Regularization
por: Ding, Yuhao, et al.
Publicado: (2021)
por: Ding, Yuhao, et al.
Publicado: (2021)
Ejemplares similares
-
A Unified Framework for Center-based Clustering of Distributed Data
por: Armacki, Aleksandar, et al.
Publicado: (2024) -
Sharp High-Probability Rates for Nonlinear SGD under Heavy-Tailed Noise via Symmetrization
por: Armacki, Aleksandar, et al.
Publicado: (2025) -
High-probability Convergence Bounds for Nonlinear Stochastic Gradient Descent Under Heavy-tailed Noise
por: Armacki, Aleksandar, et al.
Publicado: (2023) -
Large Deviation Upper Bounds and Improved MSE Rates of Nonlinear SGD: Heavy-tailed Noise and Power of Symmetry
por: Armacki, Aleksandar, et al.
Publicado: (2024) -
Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization
por: Armacki, Aleksandar, et al.
Publicado: (2026)