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Submodular Maximization Approaches for Equitable Client Selection in Federated Learning

Submodular Maximization Approaches for Equitable Client Selection in Federated Learning

Andrés Catalino Castillo Jiménez
Ege C. Kaya
Lintao Ye
Abolfazl Hashemi
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Papers citing "Submodular Maximization Approaches for Equitable Client Selection in Federated Learning"

3 / 3 papers shown
Title
A Field Guide to Federated Optimization
A Field Guide to Federated Optimization
Jianyu Wang
Zachary B. Charles
Zheng Xu
Gauri Joshi
H. B. McMahan
...
Mi Zhang
Tong Zhang
Chunxiang Zheng
Chen Zhu
Wennan Zhu
FedML
252
415
0
14 Jul 2021
Local SGD Converges Fast and Communicates Little
Local SGD Converges Fast and Communicates Little
Sebastian U. Stich
FedML
152
1,056
0
24 May 2018
Communication-Efficient Learning of Deep Networks from Decentralized Data
Communication-Efficient Learning of Deep Networks from Decentralized Data
H. B. McMahan
Eider Moore
Daniel Ramage
S. Hampson
Blaise Agüera y Arcas
FedML
234
17,328
0
17 Feb 2016

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