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FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selection
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FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selection

1 July 2024
Jiaxiang Geng
Boyu Li
Xiaoqi Qin
Yixuan Li
Liang Li
Yanzhao Hou
Miao Pan
Author Contacts:
lelegjx@bupt.edu.cnliboyu@bupt.edu.cnxiaoqiqin@bupt.edu.cnliyixuan@tyut.edu.cnlil03@pcl.ac.cnhouyanzhao@bupt.edu.cnmpan2@uh.edu
    FedML
ArXiv (abs)PDFHTML

Papers citing "FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selection"

23 / 23 papers shown
Title
FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model
  Extraction
FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction
Samiul Alam
Luyang Liu
Ming Yan
Mi Zhang
160
150
0
03 Dec 2022
FedBalancer: Data and Pace Control for Efficient Federated Learning on
  Heterogeneous Clients
FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clients
Jaemin Shin
Yuanchun Li
Yunxin Liu
Sung-Ju Lee
FedML
55
75
0
05 Jan 2022
Tackling System and Statistical Heterogeneity for Federated Learning
  with Adaptive Client Sampling
Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling
Bing Luo
Wenli Xiao
Shiqiang Wang
Jianwei Huang
Leandros Tassiulas
FedML
84
176
0
21 Dec 2021
Mobile-Former: Bridging MobileNet and Transformer
Mobile-Former: Bridging MobileNet and Transformer
Yinpeng Chen
Xiyang Dai
Dongdong Chen
Mengchen Liu
Xiaoyi Dong
Lu Yuan
Zicheng Liu
ViT
258
488
0
12 Aug 2021
Clustered Sampling: Low-Variance and Improved Representativity for
  Clients Selection in Federated Learning
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning
Yann Fraboni
Richard Vidal
Laetitia Kameni
Marco Lorenzi
FedML
60
193
0
12 May 2021
Device Sampling for Heterogeneous Federated Learning: Theory,
  Algorithms, and Implementation
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation
Su Wang
Mengyuan Lee
Seyyedali Hosseinalipour
Roberto Morabito
M. Chiang
Christopher G. Brinton
FedML
125
112
0
04 Jan 2021
To Talk or to Work: Flexible Communication Compression for Energy
  Efficient Federated Learning over Heterogeneous Mobile Edge Devices
To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices
Liang Li
Dian Shi
Ronghui Hou
Hui Li
Miao Pan
Zhu Han
FedML
62
151
0
22 Dec 2020
Oort: Efficient Federated Learning via Guided Participant Selection
Oort: Efficient Federated Learning via Guided Participant Selection
Fan Lai
Xiangfeng Zhu
H. Madhyastha
Mosharaf Chowdhury
FedMLOODD
122
275
0
12 Oct 2020
HeteroFL: Computation and Communication Efficient Federated Learning for
  Heterogeneous Clients
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients
Enmao Diao
Jie Ding
Vahid Tarokh
FedML
96
558
0
03 Oct 2020
Scheduling for Cellular Federated Edge Learning with Importance and
  Channel Awareness
Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness
Jinke Ren
Yinghui He
Dingzhu Wen
Guanding Yu
Kaibin Huang
Dongning Guo
88
196
0
01 Apr 2020
Convergence of Update Aware Device Scheduling for Federated Learning at
  the Wireless Edge
Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge
M. Amiri
Deniz Gunduz
Sanjeev R. Kulkarni
H. Vincent Poor
123
173
0
28 Jan 2020
Federated Learning for Ranking Browser History Suggestions
Federated Learning for Ranking Browser History Suggestions
Florian Hartmann
Sunah Suh
Arkadiusz Komarzewski
Tim Smith
I. Segall
FedML
51
55
0
26 Nov 2019
On the Convergence of FedAvg on Non-IID Data
On the Convergence of FedAvg on Non-IID Data
Xiang Li
Kaixuan Huang
Wenhao Yang
Shusen Wang
Zhihua Zhang
FedML
145
2,334
0
04 Jul 2019
Faster Distributed Deep Net Training: Computation and Communication
  Decoupled Stochastic Gradient Descent
Faster Distributed Deep Net Training: Computation and Communication Decoupled Stochastic Gradient Descent
Shuheng Shen
Linli Xu
Jingchang Liu
Xianfeng Liang
Yifei Cheng
ODLFedML
46
24
0
28 Jun 2019
Broadband Analog Aggregation for Low-Latency Federated Edge Learning
  (Extended Version)
Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)
Guangxu Zhu
Yong Wang
Kaibin Huang
FedML
69
643
0
30 Dec 2018
LEAF: A Benchmark for Federated Settings
LEAF: A Benchmark for Federated Settings
S. Caldas
Sai Meher Karthik Duddu
Peter Wu
Tian Li
Jakub Konecný
H. B. McMahan
Virginia Smith
Ameet Talwalkar
FedML
147
1,421
0
03 Dec 2018
Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep
  Net Training
Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training
Youjie Li
Hang Qiu
Songze Li
A. Avestimehr
Nam Sung Kim
Alex Schwing
FedML
64
104
0
08 Nov 2018
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed
  Learning
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning
Tianyi Chen
G. Giannakis
Tao Sun
W. Yin
55
298
0
25 May 2018
Client Selection for Federated Learning with Heterogeneous Resources in
  Mobile Edge
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
Takayuki Nishio
Ryo Yonetani
FedML
120
1,404
0
23 Apr 2018
TicTac: Accelerating Distributed Deep Learning with Communication
  Scheduling
TicTac: Accelerating Distributed Deep Learning with Communication Scheduling
Sayed Hadi Hashemi
Sangeetha Abdu Jyothi
R. Campbell
41
197
0
08 Mar 2018
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep
  Learning
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning
W. Wen
Cong Xu
Feng Yan
Chunpeng Wu
Yandan Wang
Yiran Chen
Hai Helen Li
140
989
0
22 May 2017
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB
  model size
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
F. Iandola
Song Han
Matthew W. Moskewicz
Khalid Ashraf
W. Dally
Kurt Keutzer
153
7,486
0
24 Feb 2016
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
406
17,486
0
17 Feb 2016
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