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MUD-PQFed: Towards Malicious User Detection in Privacy-Preserving
  Quantized Federated Learning

MUD-PQFed: Towards Malicious User Detection in Privacy-Preserving Quantized Federated Learning

19 July 2022
Hua Ma
Qun Li
Yifeng Zheng
Zhi Zhang
Xiaoning Liu
Yan Gao
S. Al-Sarawi
Derek Abbott
    FedML
ArXivPDFHTML

Papers citing "MUD-PQFed: Towards Malicious User Detection in Privacy-Preserving Quantized Federated Learning"

4 / 4 papers shown
Title
FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving
  Federated Learning with Byzantine Users
FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users
Y. Rahulamathavan
Charuka Herath
Xiaolan Liu
S. Lambotharan
Carsten Maple
25
12
0
08 Jun 2023
Vertical Federated Learning: Taxonomies, Threats, and Prospects
Vertical Federated Learning: Taxonomies, Threats, and Prospects
Qun Li
Chandra Thapa
Lawrence Ong
Yifeng Zheng
Hua Ma
S. Çamtepe
Anmin Fu
Yan Gao
FedML
21
9
0
03 Feb 2023
FedPAQ: A Communication-Efficient Federated Learning Method with
  Periodic Averaging and Quantization
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization
Amirhossein Reisizadeh
Aryan Mokhtari
Hamed Hassani
Ali Jadbabaie
Ramtin Pedarsani
FedML
157
756
0
28 Sep 2019
Analyzing Federated Learning through an Adversarial Lens
Analyzing Federated Learning through an Adversarial Lens
A. Bhagoji
Supriyo Chakraborty
Prateek Mittal
S. Calo
FedML
177
1,014
0
29 Nov 2018
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