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Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks
  under Federated Learning, A Survey and Taxonomy

Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy

16 May 2024
Yichuan Shi
Olivera Kotevska
Viktor Reshniak
Abhishek Singh
Ramesh Raskar
    AAML
ArXivPDFHTML

Papers citing "Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy"

3 / 3 papers shown
Title
Fishing for User Data in Large-Batch Federated Learning via Gradient
  Magnification
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification
Yuxin Wen
Jonas Geiping
Liam H. Fowl
Micah Goldblum
Tom Goldstein
FedML
79
92
0
01 Feb 2022
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning
  for Language Models
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models
Liam H. Fowl
Jonas Geiping
Steven Reich
Yuxin Wen
Wojtek Czaja
Micah Goldblum
Tom Goldstein
FedML
71
56
0
29 Jan 2022
When the Curious Abandon Honesty: Federated Learning Is Not Private
When the Curious Abandon Honesty: Federated Learning Is Not Private
Franziska Boenisch
Adam Dziedzic
R. Schuster
Ali Shahin Shamsabadi
Ilia Shumailov
Nicolas Papernot
FedML
AAML
69
181
0
06 Dec 2021
1