ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2208.10161
  4. Cited By
MUDGUARD: Taming Malicious Majorities in Federated Learning using
  Privacy-Preserving Byzantine-Robust Clustering

MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering

22 August 2022
Rui Wang
Xingkai Wang
H. Chen
Jérémie Decouchant
S. Picek
Z. Liu
K. Liang
ArXivPDFHTML

Papers citing "MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering"

3 / 3 papers shown
Title
Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients
Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients
Yuncong Zuo
Bart Cox
Lydia Y. Chen
Jérémie Decouchant
29
0
0
03 Jun 2024
FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping
FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping
Xiaoyu Cao
Minghong Fang
Jia Liu
Neil Zhenqiang Gong
FedML
106
611
0
27 Dec 2020
Analyzing Federated Learning through an Adversarial Lens
Analyzing Federated Learning through an Adversarial Lens
A. Bhagoji
Supriyo Chakraborty
Prateek Mittal
S. Calo
FedML
177
1,031
0
29 Nov 2018
1