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Astraea: Self-balancing Federated Learning for Improving Classification
  Accuracy of Mobile Deep Learning Applications

Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applications

2 July 2019
Moming Duan
Duo Liu
Xianzhang Chen
Yujuan Tan
Jinting Ren
Lei Qiao
Liang Liang
    FedML
ArXivPDFHTML

Papers citing "Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applications"

5 / 5 papers shown
Title
Robust and Communication-Efficient Federated Learning from Non-IID Data
Robust and Communication-Efficient Federated Learning from Non-IID Data
Felix Sattler
Simon Wiedemann
K. Müller
Wojciech Samek
FedML
39
1,343
0
07 Mar 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
78
2,652
0
04 Feb 2019
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
91
1,410
0
03 Dec 2018
Federated Learning for Ultra-Reliable Low-Latency V2V Communications
Federated Learning for Ultra-Reliable Low-Latency V2V Communications
S. Samarakoon
M. Bennis
Walid Saad
Merouane Debbah
39
228
0
11 May 2018
Federated Learning: Strategies for Improving Communication Efficiency
Federated Learning: Strategies for Improving Communication Efficiency
Jakub Konecný
H. B. McMahan
Felix X. Yu
Peter Richtárik
A. Suresh
Dave Bacon
FedML
267
4,620
0
18 Oct 2016
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