First-Order Softmax Weighted Switching Gradient Method for Distributed Stochastic Minimax Optimization with Stochastic Constraints
Fuente:
arXiv
Saved in:
| Main Authors: | Luo, Zhankun, Upadhyay, Antesh, Moon, Sang Bin, Hashemi, Abolfazl |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysis
by: Chellapandi, Vishnu Pandi, et al.
Published: (2024)
by: Chellapandi, Vishnu Pandi, et al.
Published: (2024)
Distributed Deep Learning using Stochastic Gradient Staleness
by: Pham, Viet Hoang, et al.
Published: (2025)
by: Pham, Viet Hoang, et al.
Published: (2025)
Distributed Stochastic Gradient Descent with Staleness: A Stochastic Delay Differential Equation Based Framework
by: Yu, Siyuan, et al.
Published: (2024)
by: Yu, Siyuan, et al.
Published: (2024)
An Optimistic Gradient Tracking Method for Distributed Minimax Optimization
by: Huang, Yan, et al.
Published: (2025)
by: Huang, Yan, et al.
Published: (2025)
Fast Decentralized Gradient Tracking for Federated Minimax Optimization with Local Updates
by: Li, Chris Junchi
Published: (2024)
by: Li, Chris Junchi
Published: (2024)
Decentralized Gradient-Free Methods for Stochastic Non-Smooth Non-Convex Optimization
by: Lin, Zhenwei, et al.
Published: (2023)
by: Lin, Zhenwei, et al.
Published: (2023)
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)
Trustworthiness of Stochastic Gradient Descent in Distributed Learning
by: Li, Hongyang, et al.
Published: (2024)
by: Li, Hongyang, et al.
Published: (2024)
Communication-Efficient Distributed Learning via Sparse and Adaptive Stochastic Gradient
by: Deng, Xiaoge, et al.
Published: (2021)
by: Deng, Xiaoge, et al.
Published: (2021)
Optimizing Stochastic Gradient Push under Broadcast Communications
by: Nguyen, Tuan, et al.
Published: (2026)
by: Nguyen, Tuan, et al.
Published: (2026)
An Accelerated Distributed Stochastic Gradient Method with Momentum
by: Huang, Kun, et al.
Published: (2024)
by: Huang, Kun, et al.
Published: (2024)
Asynch-SGBDT: Asynchronous Parallel Stochastic Gradient Boosting Decision Tree based on Parameters Server
by: Daning, Cheng, et al.
Published: (2018)
by: Daning, Cheng, et al.
Published: (2018)
DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM
by: Gu, Shunxian, et al.
Published: (2025)
by: Gu, Shunxian, et al.
Published: (2025)
Asynchronous Federated Stochastic Optimization for Heterogeneous Objectives Under Arbitrary Delays
by: Iakovidou, Charikleia, et al.
Published: (2024)
by: Iakovidou, Charikleia, et al.
Published: (2024)
ADP-VRSGP: Decentralized Learning with Adaptive Differential Privacy via Variance-Reduced Stochastic Gradient Push
by: Wu, Xiaoming, et al.
Published: (2025)
by: Wu, Xiaoming, et al.
Published: (2025)
Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence
by: Ansaripour, Matin, et al.
Published: (2022)
by: Ansaripour, Matin, et al.
Published: (2022)
High-Dimensional Sparse Data Low-rank Representation via Accelerated Asynchronous Parallel Stochastic Gradient Descent
by: Hu, Qicong, et al.
Published: (2024)
by: Hu, Qicong, et al.
Published: (2024)
Optimizing the Optimal Weighted Average: Efficient Distributed Sparse Classification
by: Lu, Fred, et al.
Published: (2024)
by: Lu, Fred, et al.
Published: (2024)
Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression
by: He, Yutong, et al.
Published: (2023)
by: He, Yutong, et al.
Published: (2023)
Rendering Wireless Environments Useful for Gradient Estimators: A Zero-Order Stochastic Federated Learning Method
by: Mhanna, Elissa, et al.
Published: (2024)
by: Mhanna, Elissa, et al.
Published: (2024)
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)
CoBo: Collaborative Learning via Bilevel Optimization
by: Hashemi, Diba, et al.
Published: (2024)
by: Hashemi, Diba, et al.
Published: (2024)
Federated Learning on Stochastic Neural Networks
by: Tang, Jingqiao, et al.
Published: (2025)
by: Tang, Jingqiao, et al.
Published: (2025)
A Bias-Correction Decentralized Stochastic Gradient Algorithm with Momentum Acceleration
by: Hu, Yuchen, et al.
Published: (2025)
by: Hu, Yuchen, et al.
Published: (2025)
CEDAS: A Compressed Decentralized Stochastic Gradient Method with Improved Convergence
by: Huang, Kun, et al.
Published: (2023)
by: Huang, Kun, et al.
Published: (2023)
FastSwitch: Optimizing Context Switching Efficiency in Fairness-aware Large Language Model Serving
by: Shen, Ao, et al.
Published: (2024)
by: Shen, Ao, et al.
Published: (2024)
Optimization via First-Order Switching Methods: Skew-Symmetric Dynamics and Optimistic Discretization
by: Upadhyay, Antesh, et al.
Published: (2025)
by: Upadhyay, Antesh, et al.
Published: (2025)
A Joint Approach to Local Updating and Gradient Compression for Efficient Asynchronous Federated Learning
by: Song, Jiajun, et al.
Published: (2024)
by: Song, Jiajun, et al.
Published: (2024)
Rate Analysis of Coupled Distributed Stochastic Approximation for Misspecified Optimization
by: Yang, Yaqun, et al.
Published: (2024)
by: Yang, Yaqun, et al.
Published: (2024)
Tailoring Gradient Methods for Differentially-Private Distributed Optimization
by: Wang, Yongqiang, et al.
Published: (2022)
by: Wang, Yongqiang, et al.
Published: (2022)
Stochastic Distance in Property Testing
by: Meir, Uri, et al.
Published: (2024)
by: Meir, Uri, et al.
Published: (2024)
GRAWA: Gradient-based Weighted Averaging for Distributed Training of Deep Learning Models
by: Dimlioglu, Tolga, et al.
Published: (2024)
by: Dimlioglu, Tolga, et al.
Published: (2024)
Effectiveness of Distributed Gradient Descent with Local Steps for Overparameterized Models
by: Zhu, Heng, et al.
Published: (2024)
by: Zhu, Heng, et al.
Published: (2024)
A Stochastic Approximation Approach for Efficient Decentralized Optimization on Random Networks
by: Yau, Chung-Yiu, et al.
Published: (2024)
by: Yau, Chung-Yiu, et al.
Published: (2024)
Improved Quantization Strategies for Managing Heavy-tailed Gradients in Distributed Learning
by: Yan, Guangfeng, et al.
Published: (2024)
by: Yan, Guangfeng, et al.
Published: (2024)
Distributed Learning based on 1-Bit Gradient Coding in the Presence of Stragglers
by: Li, Chengxi, et al.
Published: (2024)
by: Li, Chengxi, et al.
Published: (2024)
Runtime-Orchestrated Second-Order Optimization for Scalable LLM Training
by: Lu, Yishun, et al.
Published: (2026)
by: Lu, Yishun, et al.
Published: (2026)
Preserving Near-Optimal Gradient Sparsification Cost for Scalable Distributed Deep Learning
by: Yoon, Daegun, et al.
Published: (2024)
by: Yoon, Daegun, et al.
Published: (2024)
Stochastic Modeling for Energy-Efficient Edge Infrastructure
by: Rossi, Fabio Diniz
Published: (2025)
by: Rossi, Fabio Diniz
Published: (2025)
A Dynamic Weighting Strategy to Mitigate Worker Node Failure in Distributed Deep Learning
by: Xu, Yuesheng, et al.
Published: (2024)
by: Xu, Yuesheng, et al.
Published: (2024)
Similar Items
-
FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysis
by: Chellapandi, Vishnu Pandi, et al.
Published: (2024) -
Distributed Deep Learning using Stochastic Gradient Staleness
by: Pham, Viet Hoang, et al.
Published: (2025) -
Distributed Stochastic Gradient Descent with Staleness: A Stochastic Delay Differential Equation Based Framework
by: Yu, Siyuan, et al.
Published: (2024) -
An Optimistic Gradient Tracking Method for Distributed Minimax Optimization
by: Huang, Yan, et al.
Published: (2025) -
Fast Decentralized Gradient Tracking for Federated Minimax Optimization with Local Updates
by: Li, Chris Junchi
Published: (2024)