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DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning

DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning

2 May 2023
Wenqiang Sun
Sen Li
Yuchang Sun
Jun Zhang
    FedMLAAML
ArXiv (abs)PDFHTML

Papers citing "DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning"

6 / 6 papers shown
Title
Provably Secure Federated Learning against Malicious Clients
Provably Secure Federated Learning against Malicious Clients
Xiaoyu Cao
Jinyuan Jia
Neil Zhenqiang Gong
FedML
76
137
0
03 Feb 2021
Can You Really Backdoor Federated Learning?
Can You Really Backdoor Federated Learning?
Ziteng Sun
Peter Kairouz
A. Suresh
H. B. McMahan
FedML
75
574
0
18 Nov 2019
FedMD: Heterogenous Federated Learning via Model Distillation
FedMD: Heterogenous Federated Learning via Model Distillation
Daliang Li
Junpu Wang
FedML
98
861
0
08 Oct 2019
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
Xinyun Chen
Chang-rui Liu
Yue Liu
Kimberly Lu
Basel Alomair
AAMLSILM
143
1,852
0
15 Dec 2017
BadNets: Identifying Vulnerabilities in the Machine Learning Model
  Supply Chain
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
Tianyu Gu
Brendan Dolan-Gavitt
S. Garg
SILM
127
1,782
0
22 Aug 2017
Communication-Efficient Learning of Deep Networks from Decentralized
  Data
Communication-Efficient Learning of Deep Networks from Decentralized Data
H. B. McMahan
Eider Moore
Daniel Ramage
S. Hampson
Blaise Agüera y Arcas
FedML
406
17,559
0
17 Feb 2016
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