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Fake or Compromised? Making Sense of Malicious Clients in Federated
  Learning

Fake or Compromised? Making Sense of Malicious Clients in Federated Learning

10 March 2024
Hamid Mozaffari
Sunav Choudhary
Amir Houmansadr
ArXivPDFHTML

Papers citing "Fake or Compromised? Making Sense of Malicious Clients in Federated Learning"

4 / 4 papers shown
Title
FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?
FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?
Marco Bornstein
Amrit Singh Bedi
Abdirisak Mohamed
Furong Huang
FedML
36
0
0
22 May 2024
Federated Evaluation and Tuning for On-Device Personalization: System
  Design & Applications
Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications
Matthias Paulik
M. Seigel
Henry Mason
Dominic Telaar
Joris Kluivers
...
Dominic Hughes
O. Javidbakht
Fei Dong
Rehan Rishi
Stanley Hung
FedML
177
126
0
16 Feb 2021
IBM Federated Learning: an Enterprise Framework White Paper V0.1
IBM Federated Learning: an Enterprise Framework White Paper V0.1
Heiko Ludwig
Nathalie Baracaldo
Gegi Thomas
Yi Zhou
Ali Anwar
...
Sean Laguna
Mikhail Yurochkin
Mayank Agarwal
Ebube Chuba
Annie Abay
FedML
131
157
0
22 Jul 2020
Analyzing Federated Learning through an Adversarial Lens
Analyzing Federated Learning through an Adversarial Lens
A. Bhagoji
Supriyo Chakraborty
Prateek Mittal
S. Calo
FedML
179
1,032
0
29 Nov 2018
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