Image de couverture de livre

Federated Optimization with Doubly Regularized Drift Correction

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Jiang, Xiaowen, Rodomanov, Anton, Stich, Sebastian U.
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Optimization and Control
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Stabilized Proximal-Point Methods for Federated Optimization
par: Jiang, Xiaowen, et autres
Publié: (2024)

Non-Convex Federated Optimization under Cost-Aware Client Selection
par: Jiang, Xiaowen, et autres
Publié: (2025)

Exploiting Similarity for Computation and Communication-Efficient Decentralized Optimization
par: Takezawa, Yuki, et autres
Publié: (2025)

Non-convex Stochastic Composite Optimization with Polyak Momentum
par: Gao, Yuan, et autres
Publié: (2024)

Accelerated Distributed Optimization with Compression and Error Feedback
par: Gao, Yuan, et autres
Publié: (2025)

Universality of AdaGrad Stepsizes for Stochastic Optimization: Inexact Oracle, Acceleration and Variance Reduction
par: Rodomanov, Anton, et autres
Publié: (2024)

Composite Optimization with Error Feedback: the Dual Averaging Approach
par: Gao, Yuan, et autres
Publié: (2025)

FedMuon: Federated Learning with Bias-corrected LMO-based Optimization
par: Takezawa, Yuki, et autres
Publié: (2025)

DADA: Dual Averaging with Distance Adaptation
par: Moshtaghifar, Mohammad, et autres
Publié: (2025)

Locally Adaptive Federated Learning
par: Mukherjee, Sohom, et autres
Publié: (2023)

Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods
par: Vankov, Daniil, et autres
Publié: (2024)

Scalable Decentralized Learning with Teleportation
par: Takezawa, Yuki, et autres
Publié: (2025)

Efficient Gradient Methods for Distributed Saddle Problems
par: Luo, Ruichen, et autres
Publié: (2026)

Towards Faster Decentralized Stochastic Optimization with Communication Compression
par: Islamov, Rustem, et autres
Publié: (2024)

Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates
par: Zhang, Siqi, et autres
Publié: (2023)

On Convergence of Incremental Gradient for Non-Convex Smooth Functions
par: Koloskova, Anastasia, et autres
Publié: (2023)

Global Complexity Analysis of BFGS
par: Rodomanov, Anton
Publié: (2024)

Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis
par: Luo, Ruichen, et autres
Publié: (2025)

TAMUNA: Doubly Accelerated Distributed Optimization with Local Training, Compression, and Partial Participation
par: Condat, Laurent, et autres
Publié: (2023)

Demystifying SGD with Doubly Stochastic Gradients
par: Kim, Kyurae, et autres
Publié: (2024)

Stability of Data-Dependent Ridge-Regularization for Inverse Problems
par: Neumayer, Sebastian, et autres
Publié: (2024)

Regularized Gauss-Newton for Optimizing Overparameterized Neural Networks
par: Adeoye, Adeyemi D., et autres
Publié: (2024)

On Generalization and Regularization via Wasserstein Distributionally Robust Optimization
par: Wu, Qinyu, et autres
Publié: (2022)

Deceptive Sequential Decision-Making via Regularized Policy Optimization
par: Kim, Yerin, et autres
Publié: (2025)

Federated Composite Saddle Point Optimization
par: Bai, Site, et autres
Publié: (2023)

Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization
par: Iyengar, Garud, et autres
Publié: (2023)

A Proximal Modified Quasi-Newton Method for Nonsmooth Regularized Optimization
par: Diouane, Youssef, et autres
Publié: (2024)

Smoothing the Edges: Smooth Optimization for Sparse Regularization using Hadamard Overparametrization
par: Kolb, Chris, et autres
Publié: (2023)

Neural Network Training Techniques Regularize Optimization Trajectory: An Empirical Study
par: Chen, Cheng, et autres
Publié: (2020)

Natural Policy Gradient as Doubly Smoothed Policy Iteration: A Bellman-Operator Framework
par: Nanda, Phalguni, et autres
Publié: (2026)

Adaptive Federated Minimax Optimization with Lower Complexities
par: Huang, Feihu, et autres
Publié: (2022)

On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization
par: Liang, Ling, et autres
Publié: (2024)

Nested Stochastic Algorithm for Generalized Sinkhorn distance-Regularized Distributionally Robust Optimization
par: Yang, Yufeng, et autres
Publié: (2025)

A Regularized Newton Method for Nonconvex Optimization with Global and Local Complexity Guarantees
par: Zhou, Yuhao, et autres
Publié: (2025)

Universal Gradient Methods for Stochastic Convex Optimization
par: Rodomanov, Anton, et autres
Publié: (2024)

Scalable DC Optimization via Adaptive Frank-Wolfe Algorithms
par: Pokutta, Sebastian
Publié: (2025)

Subspace Optimization for Efficient Federated Learning under Heterogeneous Data
par: Zhu, Shuchen, et autres
Publié: (2026)

Controlling the Flow: Stability and Convergence for Stochastic Gradient Descent with Decaying Regularization
par: Kassing, Sebastian, et autres
Publié: (2025)

Spectral Preconditioning for Gradient Methods on Graded Non-convex Functions
par: Doikov, Nikita, et autres
Publié: (2024)

Towards Robust Interpretable Surrogates for Optimization
par: Goerigk, Marc, et autres
Publié: (2024)