Imagen de Portada

Stabilized Proximal-Point Methods for Federated Optimization

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Jiang, Xiaowen, Rodomanov, Anton, Stich, Sebastian U.
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Optimization and Control
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Federated Optimization with Doubly Regularized Drift Correction
por: Jiang, Xiaowen, et al.
Publicado: (2024)

Non-Convex Federated Optimization under Cost-Aware Client Selection
por: Jiang, Xiaowen, et al.
Publicado: (2025)

Exploiting Similarity for Computation and Communication-Efficient Decentralized Optimization
por: Takezawa, Yuki, et al.
Publicado: (2025)

Non-convex Stochastic Composite Optimization with Polyak Momentum
por: Gao, Yuan, et al.
Publicado: (2024)

Accelerated Distributed Optimization with Compression and Error Feedback
por: Gao, Yuan, et al.
Publicado: (2025)

Universality of AdaGrad Stepsizes for Stochastic Optimization: Inexact Oracle, Acceleration and Variance Reduction
por: Rodomanov, Anton, et al.
Publicado: (2024)

Composite Optimization with Error Feedback: the Dual Averaging Approach
por: Gao, Yuan, et al.
Publicado: (2025)

FedMuon: Federated Learning with Bias-corrected LMO-based Optimization
por: Takezawa, Yuki, et al.
Publicado: (2025)

DADA: Dual Averaging with Distance Adaptation
por: Moshtaghifar, Mohammad, et al.
Publicado: (2025)

Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods
por: Vankov, Daniil, et al.
Publicado: (2024)

Efficient Gradient Methods for Distributed Saddle Problems
por: Luo, Ruichen, et al.
Publicado: (2026)

Locally Adaptive Federated Learning
por: Mukherjee, Sohom, et al.
Publicado: (2023)

Scalable Decentralized Learning with Teleportation
por: Takezawa, Yuki, et al.
Publicado: (2025)

Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates
por: Zhang, Siqi, et al.
Publicado: (2023)

Towards Faster Decentralized Stochastic Optimization with Communication Compression
por: Islamov, Rustem, et al.
Publicado: (2024)

On Convergence of Incremental Gradient for Non-Convex Smooth Functions
por: Koloskova, Anastasia, et al.
Publicado: (2023)

Proximal Point Method for Online Saddle Point Problem
por: Meng, Qing-xin, et al.
Publicado: (2024)

Variance Reduction and Low Sample Complexity in Stochastic Optimization via Proximal Point Method
por: Liang, Jiaming
Publicado: (2024)

Global Complexity Analysis of BFGS
por: Rodomanov, Anton
Publicado: (2024)

Stochastic Newton Proximal Extragradient Method
por: Jiang, Ruichen, et al.
Publicado: (2024)

A Unified Theory of Stochastic Proximal Point Methods without Smoothness
por: Richtárik, Peter, et al.
Publicado: (2024)

Federated Composite Saddle Point Optimization
por: Bai, Site, et al.
Publicado: (2023)

The Ball-Proximal (="Broximal") Point Method: a New Algorithm, Convergence Theory, and Applications
por: Gruntkowska, Kaja, et al.
Publicado: (2025)

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

A Proximal Modified Quasi-Newton Method for Nonsmooth Regularized Optimization
por: Diouane, Youssef, et al.
Publicado: (2024)

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

Proximal Oracles for Optimization and Sampling
por: Liang, Jiaming, et al.
Publicado: (2024)

A Proximal Gradient Method With Probabilistic Multi-Gossip Communications for Decentralized Composite Optimization
por: Guo, Luyao, et al.
Publicado: (2023)

Adaptive Proximal Gradient Method for Convex Optimization
por: Malitsky, Yura, et al.
Publicado: (2023)

Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis
por: Luo, Ruichen, et al.
Publicado: (2025)

An Inexact Weighted Proximal Trust-Region Method
por: Maia, Leandro Farias, et al.
Publicado: (2026)

Universal Gradient Methods for Stochastic Convex Optimization
por: Rodomanov, Anton, et al.
Publicado: (2024)

SPAM: Stochastic Proximal Point Method with Momentum Variance Reduction for Non-convex Cross-Device Federated Learning
por: Karagulyan, Avetik, et al.
Publicado: (2024)

Exploring New Frontiers in Vertical Federated Learning: the Role of Saddle Point Reformulation
por: Beznosikov, Aleksandr, et al.
Publicado: (2026)

Spectral Preconditioning for Gradient Methods on Graded Non-convex Functions
por: Doikov, Nikita, et al.
Publicado: (2024)

Escaping Saddle Points for Nonsmooth Weakly Convex Functions via Perturbed Proximal Algorithms
por: Huang, Minhui, et al.
Publicado: (2021)

Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning
por: Ding, Kuangyu, et al.
Publicado: (2023)

Non-Euclidean Broximal Point Method: A Blueprint for Geometry-Aware Optimization
por: Gruntkowska, Kaja, et al.
Publicado: (2025)

The Stochastic Proximal Distance Algorithm
por: Jiang, Haoyu, et al.
Publicado: (2022)

Stability of the Monge Map in Semi-Dual Optimal Transport
por: Selitskiy, Anton, et al.
Publicado: (2026)