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A Two-Stage Data Selection Framework for Data-Efficient Model Training on Edge Devices
v1v2 (latest)

A Two-Stage Data Selection Framework for Data-Efficient Model Training on Edge Devices

22 May 2025
Chen Gong
Rui Xing
Zhenzhe Zheng
Fan Wu
ArXiv (abs)PDFHTML

Papers citing "A Two-Stage Data Selection Framework for Data-Efficient Model Training on Edge Devices"

26 / 26 papers shown
Title
ElasticTrainer: Speeding Up On-Device Training with Runtime Elastic
  Tensor Selection
ElasticTrainer: Speeding Up On-Device Training with Runtime Elastic Tensor Selection
Kai Huang
Boyuan Yang
Wei Gao
89
21
0
21 Dec 2023
GIO: Gradient Information Optimization for Training Dataset Selection
GIO: Gradient Information Optimization for Training Dataset Selection
Dante Everaert
Christopher Potts
102
6
0
20 Jun 2023
To Store or Not? Online Data Selection for Federated Learning with
  Limited Storage
To Store or Not? Online Data Selection for Federated Learning with Limited Storage
Chen Gong
Zhenzhe Zheng
Yunfeng Shao
Bingshuai Li
Fan Wu
Guihai Chen
81
18
0
01 Sep 2022
Adaptive Second Order Coresets for Data-efficient Machine Learning
Adaptive Second Order Coresets for Data-efficient Machine Learning
Omead Brandon Pooladzandi
David Davini
Baharan Mirzasoleiman
94
64
0
28 Jul 2022
FedSS: Federated Learning with Smart Selection of clients
FedSS: Federated Learning with Smart Selection of clients
Ammar Tahir
Yongzhou Chen
Prashanti Nilayam
FedML
41
5
0
10 Jul 2022
Boosting DNN Cold Inference on Edge Devices
Boosting DNN Cold Inference on Edge Devices
Rongjie Yi
Ting Cao
Ao Zhou
Xiao Ma
Shangguang Wang
Mengwei Xu
365
7
0
15 Jun 2022
Enabling On-Device Smartphone GPU based Training: Lessons Learned
Enabling On-Device Smartphone GPU based Training: Lessons Learned
Anish Das
Young D. Kwon
Jagmohan Chauhan
Cecilia Mascolo
3DH
67
11
0
21 Feb 2022
Choosing the Sample with Lowest Loss makes SGD Robust
Choosing the Sample with Lowest Loss makes SGD Robust
Vatsal Shah
Xiaoxia Wu
Sujay Sanghavi
40
43
0
10 Jan 2020
SurveilEdge: Real-time Video Query based on Collaborative Cloud-Edge
  Deep Learning
SurveilEdge: Real-time Video Query based on Collaborative Cloud-Edge Deep Learning
Shibo Wang
Shusen Yang
Cong Zhao
48
60
0
04 Jan 2020
Selection via Proxy: Efficient Data Selection for Deep Learning
Selection via Proxy: Efficient Data Selection for Deep Learning
Cody Coleman
Christopher Yeh
Stephen Mussmann
Baharan Mirzasoleiman
Peter Bailis
Percy Liang
J. Leskovec
Matei A. Zaharia
98
349
0
26 Jun 2019
Data Shapley: Equitable Valuation of Data for Machine Learning
Data Shapley: Equitable Valuation of Data for Machine Learning
Amirata Ghorbani
James Zou
TDIFedML
78
789
0
05 Apr 2019
Towards Federated Learning at Scale: System Design
Towards Federated Learning at Scale: System Design
Keith Bonawitz
Hubert Eichner
W. Grieskamp
Dzmitry Huba
A. Ingerman
...
H. B. McMahan
Timon Van Overveldt
David Petrou
Daniel Ramage
Jason Roselander
FedML
126
2,674
0
04 Feb 2019
A First Look at Deep Learning Apps on Smartphones
A First Look at Deep Learning Apps on Smartphones
Mengwei Xu
Jiawei Liu
Yuanqiang Liu
F. Lin
Yunxin Liu
Xuanzhe Liu
HAI
68
183
0
08 Nov 2018
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
Pete Warden
97
1,626
0
09 Apr 2018
Not All Samples Are Created Equal: Deep Learning with Importance
  Sampling
Not All Samples Are Created Equal: Deep Learning with Importance Sampling
Angelos Katharopoulos
François Fleuret
101
522
0
02 Mar 2018
Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users
  in Real-Time
Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time
Chantat Eksombatchai
Pranav Jindal
Jerry Zitao Liu
Yuchen Liu
Rahul Sharma
Charles Sugnet
Mark Ulrich
J. Leskovec
VLM
79
201
0
21 Nov 2017
Don't Decay the Learning Rate, Increase the Batch Size
Don't Decay the Learning Rate, Increase the Batch Size
Samuel L. Smith
Pieter-Jan Kindermans
Chris Ying
Quoc V. Le
ODL
107
996
0
01 Nov 2017
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
Chen Sun
Abhinav Shrivastava
Saurabh Singh
Abhinav Gupta
VLM
207
2,406
0
10 Jul 2017
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Priya Goyal
Piotr Dollár
Ross B. Girshick
P. Noordhuis
Lukasz Wesolowski
Aapo Kyrola
Andrew Tulloch
Yangqing Jia
Kaiming He
3DH
128
3,685
0
08 Jun 2017
Biased Importance Sampling for Deep Neural Network Training
Biased Importance Sampling for Deep Neural Network Training
Angelos Katharopoulos
François Fleuret
59
68
0
31 May 2017
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision
  Applications
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Andrew G. Howard
Menglong Zhu
Bo Chen
Dmitry Kalenichenko
Weijun Wang
Tobias Weyand
M. Andreetto
Hartwig Adam
3DH
1.2K
20,892
0
17 Apr 2017
iCaRL: Incremental Classifier and Representation Learning
iCaRL: Incremental Classifier and Representation Learning
Sylvestre-Alvise Rebuffi
Alexander Kolesnikov
G. Sperl
Christoph H. Lampert
CLLOOD
160
3,781
0
23 Nov 2016
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
156
7,501
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
408
17,593
0
17 Feb 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.2K
194,426
0
10 Dec 2015
Stochastic Optimization with Importance Sampling
Stochastic Optimization with Importance Sampling
P. Zhao
Tong Zhang
102
346
0
13 Jan 2014
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