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. 2206.12183
  4. Cited By
"You Can't Fix What You Can't Measure": Privately Measuring Demographic
  Performance Disparities in Federated Learning

"You Can't Fix What You Can't Measure": Privately Measuring Demographic Performance Disparities in Federated Learning

24 June 2022
Marc Juárez
Aleksandra Korolova
    FedML
ArXivPDFHTML

Papers citing ""You Can't Fix What You Can't Measure": Privately Measuring Demographic Performance Disparities in Federated Learning"

11 / 11 papers shown
Title
Federated Fairness without Access to Sensitive Groups
Federated Fairness without Access to Sensitive Groups
Afroditi Papadaki
Natalia Martínez
Martín Bertrán
Guillermo Sapiro
Miguel R. D. Rodrigues
FedML
19
2
0
22 Feb 2024
Fairness and Privacy-Preserving in Federated Learning: A Survey
Fairness and Privacy-Preserving in Federated Learning: A Survey
Taki Hasan Rafi
Faiza Anan Noor
Tahmid Hussain
Dong-Kyu Chae
FedML
22
38
0
14 Jun 2023
Can Querying for Bias Leak Protected Attributes? Achieving Privacy With
  Smooth Sensitivity
Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivity
Faisal Hamman
Jiahao Chen
Sanghamitra Dutta
9
9
0
03 Nov 2022
Privacy Aware Experimentation over Sensitive Groups: A General Chi
  Square Approach
Privacy Aware Experimentation over Sensitive Groups: A General Chi Square Approach
R. Friedberg
Ryan M. Rogers
12
3
0
17 Aug 2022
Adaptive Sampling Strategies to Construct Equitable Training Datasets
Adaptive Sampling Strategies to Construct Equitable Training Datasets
William Cai
R. Encarnación
Bobbie Chern
S. Corbett-Davies
Miranda Bogen
Stevie Bergman
Sharad Goel
77
29
0
31 Jan 2022
Bias in Automated Speaker Recognition
Bias in Automated Speaker Recognition
Wiebke Toussaint
Aaron Yi Ding
CVBM
22
44
0
24 Jan 2022
Evaluating Fairness of Machine Learning Models Under Uncertain and
  Incomplete Information
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information
Pranjal Awasthi
Alex Beutel
Matthaeus Kleindessner
Jamie Morgenstern
Xuezhi Wang
FaML
49
55
0
16 Feb 2021
FedMood: Federated Learning on Mobile Health Data for Mood Detection
FedMood: Federated Learning on Mobile Health Data for Mood Detection
Xiaohang Xu
Hao Peng
Lichao Sun
Md. Zakirul Alam Bhuiyan
Lianzhong Liu
Lifang He
FedML
34
52
0
06 Feb 2021
The Future of Digital Health with Federated Learning
The Future of Digital Health with Federated Learning
Nicola Rieke
Jonny Hancox
Wenqi Li
Fausto Milletari
H. Roth
...
Ronald M. Summers
Andrew Trask
Daguang Xu
Maximilian Baust
M. Jorge Cardoso
OOD
174
1,690
0
18 Mar 2020
A Survey on Bias and Fairness in Machine Learning
A Survey on Bias and Fairness in Machine Learning
Ninareh Mehrabi
Fred Morstatter
N. Saxena
Kristina Lerman
Aram Galstyan
SyDa
FaML
286
4,143
0
23 Aug 2019
Amplification by Shuffling: From Local to Central Differential Privacy
  via Anonymity
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity
Ulfar Erlingsson
Vitaly Feldman
Ilya Mironov
A. Raghunathan
Kunal Talwar
Abhradeep Thakurta
131
416
0
29 Nov 2018
1