FedUR: Federated Learning Optimization Through Adaptive Centralized Learning Optimizers-Reference-Cited by-同舟云学术

FedUR: Federated Learning Optimization Through Adaptive Centralized Learning Optimizers

Author:

Zhang Hengrun¹^ORCID,Zeng Kai²^ORCID,Lin Shuai³^ORCID

Affiliation:

1. Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China

2. Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, USA

3. School of Economics and Management, Shanghai Institute of Technology, Shanghai, China

Funder

Shanghai Key Laboratory of Trustworthy Computing

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Signal Processing

Link

Reference52 articles.

1. Adaptive subgradient methods for online learning and stochastic optimization;duchi;J Mach Learn Res,2011

2. SCAFFOLD: Stochastic controlled averaging for federated learning;karimireddy,2019

3. Adaptive methods for nonconvex optimization;zaheer;Proc Adv Neural Inf Process Syst (NeurIPS),2018

4. Adam: A method for stochastic optimization;kingma,2014

5. Learning multiple layers of features from tiny images;krizhevsky,2009

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

2. On the Generalization Error of Byzantine-Resilient Decentralized Learning;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14