Provable Reduction in Communication Rounds for Non-Smooth Convex Federated Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Palenzuela, Karlo, Dadras, Ali, Yurtsever, Alp, Löfstedt, Tommy |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Personalized Multi-tier Federated Learning
by: Banerjee, Sourasekhar, et al.
Published: (2024)
by: Banerjee, Sourasekhar, et al.
Published: (2024)
Convex Formulations for Training Two-Layer ReLU Neural Networks
by: Prakhya, Karthik, et al.
Published: (2024)
by: Prakhya, Karthik, et al.
Published: (2024)
Implicit Bias in Matrix Factorization and its Explicit Realization in a New Architecture
by: Hou, Yikun, et al.
Published: (2025)
by: Hou, Yikun, et al.
Published: (2025)
Fairness Regularization in Federated Learning
by: Kharaghani, Zahra, et al.
Published: (2025)
by: Kharaghani, Zahra, et al.
Published: (2025)
Exact Convex Reformulations of Linear Neural Networks via Completely Positive Lifting
by: Prakhya, Karthik, et al.
Published: (2026)
by: Prakhya, Karthik, et al.
Published: (2026)
Generalized Stochastic Gradient Descent with Momentum Methods for Smooth Optimization
by: Wang, Zimeng, et al.
Published: (2026)
by: Wang, Zimeng, et al.
Published: (2026)
Provable Adaptivity of Adam under Non-uniform Smoothness
by: Wang, Bohan, et al.
Published: (2022)
by: Wang, Bohan, et al.
Published: (2022)
Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization
by: Demidovich, Yury, et al.
Published: (2024)
by: Demidovich, Yury, et al.
Published: (2024)
Momentum Benefits Non-IID Federated Learning Simply and Provably
by: Cheng, Ziheng, et al.
Published: (2023)
by: Cheng, Ziheng, et al.
Published: (2023)
On Convergence of Incremental Gradient for Non-Convex Smooth Functions
by: Koloskova, Anastasia, et al.
Published: (2023)
by: Koloskova, Anastasia, et al.
Published: (2023)
Stochastic Non-Smooth Convex Optimization with Unbounded Gradients
by: Kovalev, Dmitry
Published: (2026)
by: Kovalev, Dmitry
Published: (2026)
Revisiting Frank-Wolfe for Structured Nonconvex Optimization
by: Maskan, Hoomaan, et al.
Published: (2025)
by: Maskan, Hoomaan, et al.
Published: (2025)
Provably Convergent Federated Trilevel Learning
by: Jiao, Yang, et al.
Published: (2023)
by: Jiao, Yang, et al.
Published: (2023)
MGDA Converges under Generalized Smoothness, Provably
by: Zhang, Qi, et al.
Published: (2024)
by: Zhang, Qi, et al.
Published: (2024)
Universal Online Convex Optimization with $1$ Projection per Round
by: Yang, Wenhao, et al.
Published: (2024)
by: Yang, Wenhao, et al.
Published: (2024)
Compressed Proximal Federated Learning for Non-Convex Composite Optimization on Heterogeneous Data
by: Qiu, Pu, et al.
Published: (2026)
by: Qiu, Pu, et al.
Published: (2026)
A Provably-Correct and Robust Convex Model for Smooth Separable NMF
by: Pan, Junjun, et al.
Published: (2025)
by: Pan, Junjun, et al.
Published: (2025)
Smooth Quasar-Convex Optimization with Constraints
by: Martínez-Rubio, David
Published: (2025)
by: Martínez-Rubio, David
Published: (2025)
Provable Complexity Improvement of AdaGrad over SGD: Upper and Lower Bounds in Stochastic Non-Convex Optimization
by: Jiang, Ruichen, et al.
Published: (2024)
by: Jiang, Ruichen, et al.
Published: (2024)
Universal Adaptive Proximal Gradient Methods via Gradient Mapping Accumulation
by: Wang, Zimeng, et al.
Published: (2026)
by: Wang, Zimeng, et al.
Published: (2026)
Muon is Provably Faster with Momentum Variance Reduction
by: Qian, Xun, et al.
Published: (2025)
by: Qian, Xun, et al.
Published: (2025)
Provable Non-Convex Euclidean Distance Matrix Completion: Geometry, Reconstruction, and Robustness
by: Smith, Chandler, et al.
Published: (2025)
by: Smith, Chandler, et al.
Published: (2025)
Non-Convex Federated Optimization under Cost-Aware Client Selection
by: Jiang, Xiaowen, et al.
Published: (2025)
by: Jiang, Xiaowen, et al.
Published: (2025)
Lower Bounds and Optimal Algorithms for Non-Smooth Convex Decentralized Optimization over Time-Varying Networks
by: Kovalev, Dmitry, et al.
Published: (2024)
by: Kovalev, Dmitry, et al.
Published: (2024)
Provably Convergent Decentralized Optimization over Directed Graphs under Generalized Smoothness
by: Bo, Yanan, et al.
Published: (2026)
by: Bo, Yanan, et al.
Published: (2026)
Improved Rates for Stochastic Variance-Reduced Difference-of-Convex Algorithms
by: Nguyen, Anh Duc, et al.
Published: (2025)
by: Nguyen, Anh Duc, et al.
Published: (2025)
Non-Euclidean High-Order Smooth Convex Optimization
by: Contreras, Juan Pablo, et al.
Published: (2024)
by: Contreras, Juan Pablo, et al.
Published: (2024)
Convex and Non-convex Federated Learning with Stale Stochastic Gradients: Diminishing Step Size is All You Need
by: Zheng, Xinran, et al.
Published: (2026)
by: Zheng, Xinran, et al.
Published: (2026)
Non-Smooth Weakly-Convex Finite-sum Coupled Compositional Optimization
by: Hu, Quanqi, et al.
Published: (2023)
by: Hu, Quanqi, et al.
Published: (2023)
An Accelerated Gradient Method for Convex Smooth Simple Bilevel Optimization
by: Cao, Jincheng, et al.
Published: (2024)
by: Cao, Jincheng, et al.
Published: (2024)
Online (Non-)Convex Learning via Tempered Optimism
by: Haddouche, Maxime, et al.
Published: (2023)
by: Haddouche, Maxime, et al.
Published: (2023)
Federated Optimization of Smooth Loss Functions
by: Jadbabaie, Ali, et al.
Published: (2022)
by: Jadbabaie, Ali, et al.
Published: (2022)
Federated Frank-Wolfe Algorithm
by: Dadras, Ali, et al.
Published: (2024)
by: Dadras, Ali, et al.
Published: (2024)
Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization
by: Armacki, Aleksandar, et al.
Published: (2026)
by: Armacki, Aleksandar, et al.
Published: (2026)
Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness
by: Kornilov, Nikita, et al.
Published: (2025)
by: Kornilov, Nikita, et al.
Published: (2025)
Provably Finding a Hidden Dense Submatrix among Many Planted Dense Submatrices via Convex Programming
by: Olanubi, Valentine, et al.
Published: (2026)
by: Olanubi, Valentine, et al.
Published: (2026)
Methods for Convex $(L_0,L_1)$-Smooth Optimization: Clipping, Acceleration, and Adaptivity
by: Gorbunov, Eduard, et al.
Published: (2024)
by: Gorbunov, Eduard, et al.
Published: (2024)
Tight Lower Bounds under Asymmetric High-Order Hölder Smoothness and Uniform Convexity
by: Bai, Cedar Site, et al.
Published: (2024)
by: Bai, Cedar Site, et al.
Published: (2024)
Adam-SHANG: A Convergent Adam-Type Method for Stochastic Smooth Convex Optimization
by: Yu, Yaxin, et al.
Published: (2026)
by: Yu, Yaxin, et al.
Published: (2026)
LyAm: Robust Non-Convex Optimization for Stable Learning in Noisy Environments
by: Mirzabeigi, Elmira, et al.
Published: (2025)
by: Mirzabeigi, Elmira, et al.
Published: (2025)
Similar Items
-
Personalized Multi-tier Federated Learning
by: Banerjee, Sourasekhar, et al.
Published: (2024) -
Convex Formulations for Training Two-Layer ReLU Neural Networks
by: Prakhya, Karthik, et al.
Published: (2024) -
Implicit Bias in Matrix Factorization and its Explicit Realization in a New Architecture
by: Hou, Yikun, et al.
Published: (2025) -
Fairness Regularization in Federated Learning
by: Kharaghani, Zahra, et al.
Published: (2025) -
Exact Convex Reformulations of Linear Neural Networks via Completely Positive Lifting
by: Prakhya, Karthik, et al.
Published: (2026)