FedGiA: An Efficient Hybrid Algorithm for Federated Learning
Fuente:
arXiv
Guardado en:
| Autores principales: | Zhou, Shenglong, Li, Geoffrey Ye |
|---|---|
| Formato: | Preprint |
| Publicado: |
2022
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling
por: Chen, Cheng, et al.
Publicado: (2020)
por: Chen, Cheng, et al.
Publicado: (2020)
FedSUM Family: Efficient Federated Learning Methods under Arbitrary Client Participation
por: You, Runze, et al.
Publicado: (2025)
por: You, Runze, et al.
Publicado: (2025)
FedSEA: Achieving Benefit of Parallelization in Federated Online Learning
por: Sahu, Harekrushna, et al.
Publicado: (2026)
por: Sahu, Harekrushna, et al.
Publicado: (2026)
FedMuon: Federated Learning with Bias-corrected LMO-based Optimization
por: Takezawa, Yuki, et al.
Publicado: (2025)
por: Takezawa, Yuki, et al.
Publicado: (2025)
FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Data
por: Zhou, Yuan, et al.
Publicado: (2025)
por: Zhou, Yuan, et al.
Publicado: (2025)
FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projection
por: He, Yutong, et al.
Publicado: (2026)
por: He, Yutong, et al.
Publicado: (2026)
Subspace Optimization for Efficient Federated Learning under Heterogeneous Data
por: Zhu, Shuchen, et al.
Publicado: (2026)
por: Zhu, Shuchen, et al.
Publicado: (2026)
FedSGM: A Unified Framework for Constraint Aware, Bidirectionally Compressed, Multi-Step Federated Optimization
por: Upadhyay, Antesh, et al.
Publicado: (2026)
por: Upadhyay, Antesh, et al.
Publicado: (2026)
An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization
por: Chen, Lesi, et al.
Publicado: (2022)
por: Chen, Lesi, et al.
Publicado: (2022)
A Simple, Optimal and Efficient Algorithm for Online Exp-Concave Optimization
por: Wang, Yi-Han, et al.
Publicado: (2025)
por: Wang, Yi-Han, et al.
Publicado: (2025)
Communication Efficient Federated Learning with Linear Convergence on Heterogeneous Data
por: Liu, Jie, et al.
Publicado: (2025)
por: Liu, Jie, et al.
Publicado: (2025)
Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses
por: Gao, Changyu, et al.
Publicado: (2024)
por: Gao, Changyu, et al.
Publicado: (2024)
A Communication-Efficient Decentralized Actor-Critic Algorithm
por: Ren, Xiaoxing, et al.
Publicado: (2025)
por: Ren, Xiaoxing, et al.
Publicado: (2025)
An Efficient Hybridization of Graph Representation Learning and Metaheuristics for the Constrained Incremental Graph Drawing Problem
por: Charytitsch, Bruna C. B., et al.
Publicado: (2025)
por: Charytitsch, Bruna C. B., et al.
Publicado: (2025)
0/1 Constrained Optimization Solving Sample Average Approximation for Chance Constrained Programming
por: Zhou, Shenglong, et al.
Publicado: (2022)
por: Zhou, Shenglong, et al.
Publicado: (2022)
Tighter Performance Theory of FedExProx
por: Anyszka, Wojciech, et al.
Publicado: (2024)
por: Anyszka, Wojciech, et al.
Publicado: (2024)
Near-optimal and Efficient First-Order Algorithm for Multi-Task Learning with Shared Linear Representation
por: Ding, Shihong, et al.
Publicado: (2026)
por: Ding, Shihong, et al.
Publicado: (2026)
Explicit and Non-asymptotic Query Complexities of Rank-Based Zeroth-order Algorithm on Stochastic Smooth Functions
por: Ye, Haishan
Publicado: (2025)
por: Ye, Haishan
Publicado: (2025)
AdaSwitch: An Adaptive Switching Meta-Algorithm for Learning-Augmented Bounded-Influence Problems
por: Chen, Xi, et al.
Publicado: (2025)
por: Chen, Xi, et al.
Publicado: (2025)
Federated Learning on Riemannian Manifolds: A Gradient-Free Projection-Based Approach
por: Wang, Hongye, et al.
Publicado: (2025)
por: Wang, Hongye, et al.
Publicado: (2025)
Locally Adaptive Federated Learning
por: Mukherjee, Sohom, et al.
Publicado: (2023)
por: Mukherjee, Sohom, et al.
Publicado: (2023)
On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations
por: Xiong, Guojun, et al.
Publicado: (2024)
por: Xiong, Guojun, et al.
Publicado: (2024)
From Learning to Optimize to Learning Optimization Algorithms
por: Castera, Camille, et al.
Publicado: (2024)
por: Castera, Camille, et al.
Publicado: (2024)
Provably Convergent Federated Trilevel Learning
por: Jiao, Yang, et al.
Publicado: (2023)
por: Jiao, Yang, et al.
Publicado: (2023)
Controlling Participation in Federated Learning with Feedback
por: Cummins, Michael, et al.
Publicado: (2024)
por: Cummins, Michael, et al.
Publicado: (2024)
Towards Understanding Generalization and Stability Gaps between Centralized and Decentralized Federated Learning
por: Sun, Yan, et al.
Publicado: (2023)
por: Sun, Yan, et al.
Publicado: (2023)
Adaptive Moment Estimation Optimization Algorithm Using Projection Gradient for Deep Learning
por: Li, Yongqi, et al.
Publicado: (2025)
por: Li, Yongqi, et al.
Publicado: (2025)
A Randomized Algorithm for Sparse PCA based on the Basic SDP Relaxation
por: Del Pia, Alberto, et al.
Publicado: (2025)
por: Del Pia, Alberto, et al.
Publicado: (2025)
Faster Adaptive Decentralized Learning Algorithms
por: Huang, Feihu, et al.
Publicado: (2024)
por: Huang, Feihu, et al.
Publicado: (2024)
Distributionally Robust Federated Learning with Outlier Resilience
por: Wang, Zifan, et al.
Publicado: (2025)
por: Wang, Zifan, et al.
Publicado: (2025)
Achieving Linear Speedup for Composite Federated Learning
por: Huang, Kun, et al.
Publicado: (2026)
por: Huang, Kun, et al.
Publicado: (2026)
Federated Learning Can Find Friends That Are Advantageous
por: Tupitsa, Nazarii, et al.
Publicado: (2024)
por: Tupitsa, Nazarii, et al.
Publicado: (2024)
Private Networked Federated Learning for Nonsmooth Objectives
por: Gauthier, François, et al.
Publicado: (2023)
por: Gauthier, François, et al.
Publicado: (2023)
On Performance Guarantees for Federated Learning with Personalized Constraints
por: Ebrahimi, Mohammadjavad, et al.
Publicado: (2026)
por: Ebrahimi, Mohammadjavad, et al.
Publicado: (2026)
An Efficient Global Optimization Algorithm with Adaptive Estimates of the Local Lipschitz Constants
por: D'Agostino, Danny
Publicado: (2022)
por: D'Agostino, Danny
Publicado: (2022)
Efficient Algorithms for Robust Markov Decision Processes with $s$-Rectangular Ambiguity Sets
por: Ho, Chin Pang, et al.
Publicado: (2026)
por: Ho, Chin Pang, et al.
Publicado: (2026)
An Efficient Alternating Algorithm for ReLU-based Symmetric Matrix Decomposition
por: Wang, Qingsong
Publicado: (2025)
por: Wang, Qingsong
Publicado: (2025)
A Randomized Zeroth-Order Hierarchical Framework for Heterogeneous Federated Learning
por: Qiu, Yuyang, et al.
Publicado: (2025)
por: Qiu, Yuyang, et al.
Publicado: (2025)
Mathematical Foundations of Deep Learning
por: Ye, Xiaojing
Publicado: (2026)
por: Ye, Xiaojing
Publicado: (2026)
Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models
por: Xie, Xingyu, et al.
Publicado: (2022)
por: Xie, Xingyu, et al.
Publicado: (2022)
Ejemplares similares
-
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling
por: Chen, Cheng, et al.
Publicado: (2020) -
FedSUM Family: Efficient Federated Learning Methods under Arbitrary Client Participation
por: You, Runze, et al.
Publicado: (2025) -
FedSEA: Achieving Benefit of Parallelization in Federated Online Learning
por: Sahu, Harekrushna, et al.
Publicado: (2026) -
FedMuon: Federated Learning with Bias-corrected LMO-based Optimization
por: Takezawa, Yuki, et al.
Publicado: (2025) -
FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Data
por: Zhou, Yuan, et al.
Publicado: (2025)