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Federated Learning: Strategies for Improving Communication Efficiency
v1v2 (latest)

Federated Learning: Strategies for Improving Communication Efficiency

18 October 2016
Jakub Konecný
H. B. McMahan
Felix X. Yu
Peter Richtárik
A. Suresh
Dave Bacon
    FedML
ArXiv (abs)PDFHTML

Papers citing "Federated Learning: Strategies for Improving Communication Efficiency"

50 / 1,868 papers shown
Title
FetchSGD: Communication-Efficient Federated Learning with Sketching
FetchSGD: Communication-Efficient Federated Learning with Sketching
D. Rothchild
Ashwinee Panda
Enayat Ullah
Nikita Ivkin
Ion Stoica
Vladimir Braverman
Joseph E. Gonzalez
Raman Arora
FedML
100
373
0
15 Jul 2020
Tackling the Objective Inconsistency Problem in Heterogeneous Federated
  Optimization
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
Jianyu Wang
Qinghua Liu
Hao Liang
Gauri Joshi
H. Vincent Poor
MoMeFedML
107
1,366
0
15 Jul 2020
FedBoosting: Federated Learning with Gradient Protected Boosting for
  Text Recognition
FedBoosting: Federated Learning with Gradient Protected Boosting for Text Recognition
Hanchi Ren
Jingjing Deng
Xianghua Xie
Xiaoke Ma
Yi-Cheng Wang
FedML
87
11
0
14 Jul 2020
Quality Inference in Federated Learning with Secure Aggregation
Quality Inference in Federated Learning with Secure Aggregation
Balázs Pejó
G. Biczók
FedML
86
22
0
13 Jul 2020
VAFL: a Method of Vertical Asynchronous Federated Learning
VAFL: a Method of Vertical Asynchronous Federated Learning
Tianyi Chen
Xiao Jin
Yuejiao Sun
W. Yin
FedML
123
163
0
12 Jul 2020
MeDaS: An open-source platform as service to help break the walls
  between medicine and informatics
MeDaS: An open-source platform as service to help break the walls between medicine and informatics
Liang Zhang
Johann Li
Ping Li
Xiaoyuan Lu
Peiyi Shen
Guangming Zhu
Syed Afaq Ali Shah
Bennamoun
Kun Qian
Björn W. Schuller
MedIm
67
6
0
12 Jul 2020
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
Hongyi Wang
Kartik K. Sreenivasan
Shashank Rajput
Harit Vishwakarma
Saurabh Agarwal
Jy-yong Sohn
Kangwook Lee
Dimitris Papailiopoulos
FedML
129
617
0
09 Jul 2020
Client Adaptation improves Federated Learning with Simulated Non-IID
  Clients
Client Adaptation improves Federated Learning with Simulated Non-IID Clients
Laura Rieger
Rasmus M. Th. Høegh
Lars Kai Hansen
FedML
62
6
0
09 Jul 2020
Challenges of AI in Wireless Networks for IoT
Challenges of AI in Wireless Networks for IoT
Ijaz Ahmad
Shahriar Shahabuddin
T. Kumar
E. Harjula
M. Meisel
M. Juntti
T. Sauter
M. Ylianttila
46
18
0
09 Jul 2020
BlockFLow: An Accountable and Privacy-Preserving Solution for Federated
  Learning
BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning
Vaikkunth Mugunthan
Ravi Rahman
Lalana Kagal
FedML
122
41
0
08 Jul 2020
Coded Computing for Federated Learning at the Edge
Coded Computing for Federated Learning at the Edge
Saurav Prakash
S. Dhakal
M. Akdeniz
A. Avestimehr
N. Himayat
FedML
72
11
0
07 Jul 2020
Sharing Models or Coresets: A Study based on Membership Inference Attack
Sharing Models or Coresets: A Study based on Membership Inference Attack
Hanlin Lu
Changchang Liu
T. He
Shiqiang Wang
Kevin S. Chan
MIACVFedML
58
16
0
06 Jul 2020
Experiments of Federated Learning for COVID-19 Chest X-ray Images
Experiments of Federated Learning for COVID-19 Chest X-ray Images
Boyi Liu
Bingjie Yan
Yize Zhou
Yifan Yang
Yixian Zhang
OODFedML
69
155
0
05 Jul 2020
Harnessing Wireless Channels for Scalable and Privacy-Preserving
  Federated Learning
Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning
Anis Elgabli
Jihong Park
Chaouki Ben Issaid
M. Bennis
94
56
0
03 Jul 2020
Federated Learning and Differential Privacy: Software tools analysis,
  the Sherpa.ai FL framework and methodological guidelines for preserving data
  privacy
Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy
Nuria Rodríguez Barroso
G. Stipcich
Daniel Jiménez-López
José Antonio Ruiz-Millán
Eugenio Martínez-Cámara
Gerardo González-Seco
M. V. Luzón
M. Veganzones
Francisco Herrera
79
103
0
02 Jul 2020
On the Outsized Importance of Learning Rates in Local Update Methods
On the Outsized Importance of Learning Rates in Local Update Methods
Zachary B. Charles
Jakub Konecný
FedML
100
54
0
02 Jul 2020
Shuffle-Exchange Brings Faster: Reduce the Idle Time During
  Communication for Decentralized Neural Network Training
Shuffle-Exchange Brings Faster: Reduce the Idle Time During Communication for Decentralized Neural Network Training
Xiang Yang
FedML
22
2
0
01 Jul 2020
FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based
  IIoT Applications
FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications
Yunfei Song
Tian Liu
Tongquan Wei
Xiangfeng Wang
Zhe Tao
Mingsong Chen
108
50
0
28 Jun 2020
ByGARS: Byzantine SGD with Arbitrary Number of Attackers
ByGARS: Byzantine SGD with Arbitrary Number of Attackers
Jayanth Reddy Regatti
Hao Chen
Abhishek Gupta
FedMLAAML
72
4
0
24 Jun 2020
Exact Support Recovery in Federated Regression with One-shot
  Communication
Exact Support Recovery in Federated Regression with One-shot Communication
Adarsh Barik
Jean Honorio
FedML
60
2
0
22 Jun 2020
Open-Domain Conversational Agents: Current Progress, Open Problems, and
  Future Directions
Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions
Stephen Roller
Y-Lan Boureau
Jason Weston
Antoine Bordes
Emily Dinan
...
Kurt Shuster
Eric Michael Smith
Arthur Szlam
Jack Urbanek
Mary Williamson
LLMAGAI4CE
132
52
0
22 Jun 2020
D2P-Fed: Differentially Private Federated Learning With Efficient
  Communication
D2P-Fed: Differentially Private Federated Learning With Efficient Communication
Lun Wang
R. Jia
Dawn Song
FedML
20
0
0
22 Jun 2020
Scheduling Policy and Power Allocation for Federated Learning in NOMA
  Based MEC
Scheduling Policy and Power Allocation for Federated Learning in NOMA Based MEC
Xiang Ma
Haijian Sun
R. Hu
37
39
0
21 Jun 2020
Optimal and Practical Algorithms for Smooth and Strongly Convex
  Decentralized Optimization
Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization
D. Kovalev
Adil Salim
Peter Richtárik
73
83
0
21 Jun 2020
Federated Learning With Quantized Global Model Updates
Federated Learning With Quantized Global Model Updates
M. Amiri
Deniz Gunduz
Sanjeev R. Kulkarni
H. Vincent Poor
FedML
120
133
0
18 Jun 2020
Neural Parameter Allocation Search
Neural Parameter Allocation Search
Bryan A. Plummer
Nikoli Dryden
Julius Frost
Torsten Hoefler
Kate Saenko
129
16
0
18 Jun 2020
Is Network the Bottleneck of Distributed Training?
Is Network the Bottleneck of Distributed Training?
Zhen Zhang
Chaokun Chang
Yanghua Peng
Yida Wang
R. Arora
Xin Jin
91
71
0
17 Jun 2020
Faster Secure Data Mining via Distributed Homomorphic Encryption
Faster Secure Data Mining via Distributed Homomorphic Encryption
Junyi Li
Heng-Chiao Huang
FedML
69
21
0
17 Jun 2020
Differentially-private Federated Neural Architecture Search
Differentially-private Federated Neural Architecture Search
Ishika Singh
Haoyi Zhou
Kunlin Yang
Mengxiao Ding
Bill Lin
P. Xie
FedML
91
22
0
16 Jun 2020
Robust Federated Learning: The Case of Affine Distribution Shifts
Robust Federated Learning: The Case of Affine Distribution Shifts
Amirhossein Reisizadeh
Farzan Farnia
Ramtin Pedarsani
Ali Jadbabaie
FedMLOOD
105
167
0
16 Jun 2020
Optimal Complexity in Decentralized Training
Optimal Complexity in Decentralized Training
Yucheng Lu
Christopher De Sa
141
75
0
15 Jun 2020
Topology-aware Differential Privacy for Decentralized Image
  Classification
Topology-aware Differential Privacy for Decentralized Image Classification
Shangwei Guo
Tianwei Zhang
Guowen Xu
Hanzhou Yu
Tao Xiang
Yang Liu
85
18
0
14 Jun 2020
Understanding Unintended Memorization in Federated Learning
Understanding Unintended Memorization in Federated Learning
Om Thakkar
Swaroop Indra Ramaswamy
Rajiv Mathews
Franccoise Beaufays
FedML
93
47
0
12 Jun 2020
FedGAN: Federated Generative Adversarial Networks for Distributed Data
FedGAN: Federated Generative Adversarial Networks for Distributed Data
M. Rasouli
Tao Sun
Ram Rajagopal
FedML
108
145
0
12 Jun 2020
A Unified Analysis of Stochastic Gradient Methods for Nonconvex
  Federated Optimization
A Unified Analysis of Stochastic Gradient Methods for Nonconvex Federated Optimization
Zhize Li
Peter Richtárik
FedML
101
36
0
12 Jun 2020
Characterizing Impacts of Heterogeneity in Federated Learning upon
  Large-Scale Smartphone Data
Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data
Chengxu Yang
Qipeng Wang
Mengwei Xu
Shangguang Wang
Kaigui Bian
Yunxin Liu
Xuanzhe Liu
106
23
0
12 Jun 2020
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method
Yossi Arjevani
Joan Bruna
Bugra Can
Mert Gurbuzbalaban
Stefanie Jegelka
Hongzhou Lin
58
16
0
11 Jun 2020
SEFR: A Fast Linear-Time Classifier for Ultra-Low Power Devices
SEFR: A Fast Linear-Time Classifier for Ultra-Low Power Devices
Hamidreza Keshavarz
M. S. Abadeh
Reza Rawassizadeh
79
15
0
08 Jun 2020
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function
  Secret Sharing
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing
T. Ryffel
Pierre Tholoniat
D. Pointcheval
Francis R. Bach
FedML
173
101
0
08 Jun 2020
Decentralised Learning from Independent Multi-Domain Labels for Person
  Re-Identification
Decentralised Learning from Independent Multi-Domain Labels for Person Re-Identification
Guile Wu
S. Gong
OOD
112
31
0
07 Jun 2020
UVeQFed: Universal Vector Quantization for Federated Learning
UVeQFed: Universal Vector Quantization for Federated Learning
Nir Shlezinger
Mingzhe Chen
Yonina C. Eldar
H. Vincent Poor
Shuguang Cui
FedMLMQ
65
231
0
05 Jun 2020
Federated Learning for 6G Communications: Challenges, Methods, and
  Future Directions
Federated Learning for 6G Communications: Challenges, Methods, and Future Directions
Yi Liu
Lizhen Qu
Zehui Xiong
Jiawen Kang
Xiaofei Wang
Dusit Niyato
FedMLAI4CE
77
284
0
04 Jun 2020
Local SGD With a Communication Overhead Depending Only on the Number of
  Workers
Local SGD With a Communication Overhead Depending Only on the Number of Workers
Artin Spiridonoff
Alexander Olshevsky
I. Paschalidis
FedML
61
19
0
03 Jun 2020
AI Research Considerations for Human Existential Safety (ARCHES)
AI Research Considerations for Human Existential Safety (ARCHES)
Andrew Critch
David M. Krueger
112
54
0
30 May 2020
Meta Clustering for Collaborative Learning
Meta Clustering for Collaborative Learning
Chenglong Ye
R. Ghanadan
Jie Ding
120
4
0
29 May 2020
Synthetic Learning: Learn From Distributed Asynchronized Discriminator
  GAN Without Sharing Medical Image Data
Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data
Qi Chang
Hui Qu
Yikai Zhang
M. Sabuncu
Chao Chen
Tong Zhang
Dimitris N. Metaxas
MedIm
108
83
0
29 May 2020
Vertically Federated Graph Neural Network for Privacy-Preserving Node
  Classification
Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification
Chaochao Chen
Jun Zhou
Longfei Zheng
Huiwen Wu
Lingjuan Lyu
Hongzhi Zhang
Bingzhe Wu
Ziqi Liu
L. xilinx Wang
Xiaolin Zheng
FedML
95
100
0
25 May 2020
Reliability and Performance Assessment of Federated Learning on Clinical
  Benchmark Data
Reliability and Performance Assessment of Federated Learning on Clinical Benchmark Data
G. Lee
S. Shin
OOD
44
2
0
24 May 2020
FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity
  to Non-IID Data
FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data
Xinwei Zhang
Mingyi Hong
S. Dhople
W. Yin
Yang Liu
FedML
113
235
0
22 May 2020
Global Multiclass Classification and Dataset Construction via
  Heterogeneous Local Experts
Global Multiclass Classification and Dataset Construction via Heterogeneous Local Experts
Surin Ahn
Ayfer Özgür
Mert Pilanci
21
0
0
21 May 2020
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