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. 1910.05467
  4. Cited By
Quantification of the Leakage in Federated Learning

Quantification of the Leakage in Federated Learning

12 October 2019
Zhaorui Li
Zhicong Huang
Chaochao Chen
Cheng Hong
    FedML
    PILM
ArXivPDFHTML

Papers citing "Quantification of the Leakage in Federated Learning"

3 / 3 papers shown
Title
Survey on Federated Learning Threats: concepts, taxonomy on attacks and
  defences, experimental study and challenges
Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges
Nuria Rodríguez-Barroso
Daniel Jiménez López
M. V. Luzón
Francisco Herrera
Eugenio Martínez-Cámara
FedML
37
213
0
20 Jan 2022
From Distributed Machine Learning to Federated Learning: A Survey
From Distributed Machine Learning to Federated Learning: A Survey
Ji Liu
Jizhou Huang
Yang Zhou
Xuhong Li
Shilei Ji
Haoyi Xiong
Dejing Dou
FedML
OOD
56
244
0
29 Apr 2021
Property Inference Attacks on Convolutional Neural Networks: Influence
  and Implications of Target Model's Complexity
Property Inference Attacks on Convolutional Neural Networks: Influence and Implications of Target Model's Complexity
Mathias Parisot
Balázs Pejó
Dayana Spagnuelo
MIACV
27
33
0
27 Apr 2021
1