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Distilling On-Device Intelligence at the Network Edge

Distilling On-Device Intelligence at the Network Edge

16 August 2019
Jihong Park
Shiqiang Wang
Anis Elgabli
Seungeun Oh
Eunjeong Jeong
Han Cha
Hyesung Kim
Seong-Lyun Kim
M. Bennis
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Papers citing "Distilling On-Device Intelligence at the Network Edge"

11 / 11 papers shown
Title
Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation
  With Two-Way Mixup
Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup
Seungeun Oh
Jihong Park
Eunjeong Jeong
Hyesung Kim
M. Bennis
Seong-Lyun Kim
FedML
52
58
0
17 Jun 2020
GADMM: Fast and Communication Efficient Framework for Distributed
  Machine Learning
GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning
Anis Elgabli
Jihong Park
Amrit Singh Bedi
M. Bennis
Vaneet Aggarwal
FedML
23
82
0
30 Aug 2019
Federated Reinforcement Distillation with Proxy Experience Memory
Federated Reinforcement Distillation with Proxy Experience Memory
Han Cha
Jihong Park
Hyesung Kim
Seong-Lyun Kim
M. Bennis
54
16
0
15 Jul 2019
Multi-hop Federated Private Data Augmentation with Sample Compression
Multi-hop Federated Private Data Augmentation with Sample Compression
Eunjeong Jeong
Seungeun Oh
Jihong Park
Hyesung Kim
M. Bennis
Seong-Lyun Kim
53
17
0
15 Jul 2019
Robust Coreset Construction for Distributed Machine Learning
Robust Coreset Construction for Distributed Machine Learning
Hanlin Lu
Ming-Ju Li
T. He
Shiqiang Wang
Vijay Narayanan
Kevin S. Chan
OOD
42
25
0
11 Apr 2019
No Peek: A Survey of private distributed deep learning
No Peek: A Survey of private distributed deep learning
Praneeth Vepakomma
Tristan Swedish
Ramesh Raskar
O. Gupta
Abhimanyu Dubey
SyDa
FedML
47
100
0
08 Dec 2018
Wireless Network Intelligence at the Edge
Wireless Network Intelligence at the Edge
Jihong Park
S. Samarakoon
M. Bennis
Mérouane Debbah
87
519
0
07 Dec 2018
Communication-Efficient On-Device Machine Learning: Federated
  Distillation and Augmentation under Non-IID Private Data
Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data
Eunjeong Jeong
Seungeun Oh
Hyesung Kim
Jihong Park
M. Bennis
Seong-Lyun Kim
FedML
54
596
0
28 Nov 2018
Adaptive Federated Learning in Resource Constrained Edge Computing
  Systems
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
Shiqiang Wang
Tiffany Tuor
Theodoros Salonidis
K. Leung
C. Makaya
T. He
Kevin S. Chan
200
1,698
0
14 Apr 2018
Large scale distributed neural network training through online
  distillation
Large scale distributed neural network training through online distillation
Rohan Anil
Gabriel Pereyra
Alexandre Passos
Róbert Ormándi
George E. Dahl
Geoffrey E. Hinton
FedML
314
405
0
09 Apr 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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