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Federated Learning: Challenges, Methods, and Future Directions

Federated Learning: Challenges, Methods, and Future Directions

21 August 2019
Tian Li
Anit Kumar Sahu
Ameet Talwalkar
Virginia Smith
    FedML
ArXivPDFHTML

Papers citing "Federated Learning: Challenges, Methods, and Future Directions"

21 / 621 papers shown
Title
Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized
  Machine Learning
Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning
Anis Elgabli
Jihong Park
Amrit Singh Bedi
Chaouki Ben Issaid
M. Bennis
Vaneet Aggarwal
24
67
0
23 Oct 2019
Communication-Efficient Local Decentralized SGD Methods
Communication-Efficient Local Decentralized SGD Methods
Xiang Li
Wenhao Yang
Shusen Wang
Zhihua Zhang
30
53
0
21 Oct 2019
FedMD: Heterogenous Federated Learning via Model Distillation
FedMD: Heterogenous Federated Learning via Model Distillation
Daliang Li
Junpu Wang
FedML
42
831
0
08 Oct 2019
Open Set Medical Diagnosis
Open Set Medical Diagnosis
Viraj Prabhu
A. Kannan
Geoffrey Tso
Namit Katariya
Manish Chablani
David Sontag
X. Amatriain
23
9
0
07 Oct 2019
Soft-Label Dataset Distillation and Text Dataset Distillation
Soft-Label Dataset Distillation and Text Dataset Distillation
Ilia Sucholutsky
Matthias Schonlau
DD
30
131
0
06 Oct 2019
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task
  Optimization under Privacy Constraints
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints
Felix Sattler
K. Müller
Wojciech Samek
FedML
69
966
0
04 Oct 2019
FedPAQ: A Communication-Efficient Federated Learning Method with
  Periodic Averaging and Quantization
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization
Amirhossein Reisizadeh
Aryan Mokhtari
Hamed Hassani
Ali Jadbabaie
Ramtin Pedarsani
FedML
174
763
0
28 Sep 2019
Federated User Representation Learning
Federated User Representation Learning
D. Bui
Kshitiz Malik
Jack Goetz
Honglei Liu
Seungwhan Moon
Anuj Kumar
Kang G. Shin
FedML
30
62
0
27 Sep 2019
Improving Federated Learning Personalization via Model Agnostic Meta
  Learning
Improving Federated Learning Personalization via Model Agnostic Meta Learning
Yihan Jiang
Jakub Konecný
Keith Rush
Sreeram Kannan
FedML
21
587
0
27 Sep 2019
Model Pruning Enables Efficient Federated Learning on Edge Devices
Model Pruning Enables Efficient Federated Learning on Edge Devices
Yuang Jiang
Shiqiang Wang
Victor Valls
Bongjun Ko
Wei-Han Lee
Kin K. Leung
Leandros Tassiulas
38
445
0
26 Sep 2019
Differentially Private Meta-Learning
Differentially Private Meta-Learning
Jeffrey Li
M. Khodak
S. Caldas
Ameet Talwalkar
FedML
35
106
0
12 Sep 2019
Gradient Descent with Compressed Iterates
Gradient Descent with Compressed Iterates
Ahmed Khaled
Peter Richtárik
21
22
0
10 Sep 2019
Machine Learning at the Network Edge: A Survey
Machine Learning at the Network Edge: A Survey
M. G. Sarwar Murshed
Chris Murphy
Daqing Hou
Nazar Khan
Ganesh Ananthanarayanan
Faraz Hussain
38
378
0
31 Jul 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
82
2,283
0
04 Jul 2019
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals,
  Technology Integration, and State-of-the-Art
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art
Viet Quoc Pham
Fang Fang
H. Vu
Md. Jalil Piran
Mai Le
L. Le
W. Hwang
Z. Ding
23
596
0
20 Jun 2019
Wireless Network Intelligence at the Edge
Wireless Network Intelligence at the Edge
Jihong Park
S. Samarakoon
M. Bennis
Mérouane Debbah
21
518
0
07 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
56
1,393
0
03 Dec 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
144
1,687
0
14 Apr 2018
Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in
  Distributed SGD
Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD
Sanghamitra Dutta
Gauri Joshi
Soumyadip Ghosh
Parijat Dube
P. Nagpurkar
31
193
0
03 Mar 2018
Federated Meta-Learning with Fast Convergence and Efficient
  Communication
Federated Meta-Learning with Fast Convergence and Efficient Communication
Fei Chen
Mi Luo
Zhenhua Dong
Zhenguo Li
Xiuqiang He
FedML
34
388
0
22 Feb 2018
Optimal Distributed Online Prediction using Mini-Batches
Optimal Distributed Online Prediction using Mini-Batches
O. Dekel
Ran Gilad-Bachrach
Ohad Shamir
Lin Xiao
179
683
0
07 Dec 2010
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