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BlockFLow: An Accountable and Privacy-Preserving Solution for Federated
  Learning

BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning

8 July 2020
Vaikkunth Mugunthan
Ravi Rahman
Lalana Kagal
    FedML
ArXivPDFHTML

Papers citing "BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning"

5 / 5 papers shown
Title
Federated Variance-Reduced Stochastic Gradient Descent with Robustness
  to Byzantine Attacks
Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine Attacks
Zhaoxian Wu
Qing Ling
Tianyi Chen
G. Giannakis
FedML
AAML
52
182
0
29 Dec 2019
Decentralized & Collaborative AI on Blockchain
Decentralized & Collaborative AI on Blockchain
Justin D. Harris
Bo Waggoner
SyDa
36
108
0
16 Jul 2019
Exploiting Unintended Feature Leakage in Collaborative Learning
Exploiting Unintended Feature Leakage in Collaborative Learning
Luca Melis
Congzheng Song
Emiliano De Cristofaro
Vitaly Shmatikov
FedML
128
1,461
0
10 May 2018
Membership Inference Attacks against Machine Learning Models
Membership Inference Attacks against Machine Learning Models
Reza Shokri
M. Stronati
Congzheng Song
Vitaly Shmatikov
SLR
MIALM
MIACV
200
4,075
0
18 Oct 2016
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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