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Client Selection in Federated Learning: Convergence Analysis and
  Power-of-Choice Selection Strategies

Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies

3 October 2020
Yae Jee Cho
Jianyu Wang
Gauri Joshi
    FedML
ArXivPDFHTML

Papers citing "Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies"

13 / 163 papers shown
Title
Local Adaptivity in Federated Learning: Convergence and Consistency
Local Adaptivity in Federated Learning: Convergence and Consistency
Jianyu Wang
Zheng Xu
Zachary Garrett
Zachary B. Charles
Luyang Liu
Gauri Joshi
FedML
32
39
0
04 Jun 2021
Node Selection Toward Faster Convergence for Federated Learning on
  Non-IID Data
Node Selection Toward Faster Convergence for Federated Learning on Non-IID Data
Hongda Wu
Ping Wang
FedML
29
135
0
14 May 2021
FedCor: Correlation-Based Active Client Selection Strategy for
  Heterogeneous Federated Learning
FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning
Minxue Tang
Xuefei Ning
Yitu Wang
Jingwei Sun
Yu Wang
H. Li
Yiran Chen
FedML
27
80
0
24 Mar 2021
Heterogeneity for the Win: One-Shot Federated Clustering
Heterogeneity for the Win: One-Shot Federated Clustering
D. Dennis
Tian Li
Virginia Smith
FedML
22
146
0
01 Mar 2021
Sustainable Federated Learning
Sustainable Federated Learning
Başak Güler
Aylin Yener
14
13
0
22 Feb 2021
Energy-Harvesting Distributed Machine Learning
Energy-Harvesting Distributed Machine Learning
Başak Güler
Aylin Yener
FedML
23
15
0
10 Feb 2021
FedProf: Selective Federated Learning with Representation Profiling
FedProf: Selective Federated Learning with Representation Profiling
Wentai Wu
Ligang He
Weiwei Lin
Carsten Maple
FedML
30
1
0
02 Feb 2021
Bandit-based Communication-Efficient Client Selection Strategies for
  Federated Learning
Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning
Yae Jee Cho
Samarth Gupta
Gauri Joshi
Osman Yağan
FedML
13
67
0
14 Dec 2020
Accurate and Fast Federated Learning via Combinatorial Multi-Armed
  Bandits
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits
Taehyeon Kim
Sangmin Bae
Jin-woo Lee
Se-Young Yun
FedML
26
15
0
06 Dec 2020
Stochastic Client Selection for Federated Learning with Volatile Clients
Stochastic Client Selection for Federated Learning with Volatile Clients
Tiansheng Huang
Weiwei Lin
Li Shen
Keqin Li
Albert Y. Zomaya
FedML
14
97
0
17 Nov 2020
Optimal Client Sampling for Federated Learning
Optimal Client Sampling for Federated Learning
Wenlin Chen
Samuel Horváth
Peter Richtárik
FedML
42
190
0
26 Oct 2020
Communication-Efficient Federated Learning via Optimal Client Sampling
Communication-Efficient Federated Learning via Optimal Client Sampling
Mónica Ribero
H. Vikalo
FedML
13
92
0
30 Jul 2020
Distributed Non-Convex Optimization with Sublinear Speedup under
  Intermittent Client Availability
Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability
Yikai Yan
Chaoyue Niu
Yucheng Ding
Zhenzhe Zheng
Fan Wu
Guihai Chen
Shaojie Tang
Zhihua Wu
FedML
49
37
0
18 Feb 2020
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