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Gradient-Leaks: Understanding and Controlling Deanonymization in
  Federated Learning

Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning

15 May 2018
Tribhuvanesh Orekondy
Seong Joon Oh
Yang Zhang
Bernt Schiele
Mario Fritz
    PICV
    FedML
ArXivPDFHTML

Papers citing "Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning"

3 / 3 papers shown
Title
Byzantine Fault Tolerance in Distributed Machine Learning : a Survey
Byzantine Fault Tolerance in Distributed Machine Learning : a Survey
Djamila Bouhata
Hamouma Moumen
Moumen Hamouma
Ahcène Bounceur
AI4CE
27
7
0
05 May 2022
Federated Learning Challenges and Opportunities: An Outlook
Federated Learning Challenges and Opportunities: An Outlook
Jie Ding
Eric W. Tramel
Anit Kumar Sahu
Shuang Wu
Salman Avestimehr
Tao Zhang
FedML
33
55
0
01 Feb 2022
Analyzing Federated Learning through an Adversarial Lens
Analyzing Federated Learning through an Adversarial Lens
A. Bhagoji
Supriyo Chakraborty
Prateek Mittal
S. Calo
FedML
191
1,032
0
29 Nov 2018
1