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Achieving Fairness Across Local and Global Models in Federated Learning

Achieving Fairness Across Local and Global Models in Federated Learning

24 June 2024
Disha Makhija
Xing Han
Joydeep Ghosh
Yejin Kim
    FedML
ArXivPDFHTML

Papers citing "Achieving Fairness Across Local and Global Models in Federated Learning"

4 / 4 papers shown
Title
Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning
Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning
Rongguang Ye
Ming Tang
FedML
48
0
0
30 Apr 2025
PraFFL: A Preference-Aware Scheme in Fair Federated Learning
PraFFL: A Preference-Aware Scheme in Fair Federated Learning
Rongguang Ye
Wei-Bin Kou
Ming Tang
FedML
31
4
0
13 Apr 2024
Enforcing fairness in private federated learning via the modified method
  of differential multipliers
Enforcing fairness in private federated learning via the modified method of differential multipliers
Borja Rodríguez Gálvez
Filip Granqvist
Rogier van Dalen
M. Seigel
FedML
36
51
0
17 Sep 2021
Fair prediction with disparate impact: A study of bias in recidivism
  prediction instruments
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova
FaML
185
2,079
0
24 Oct 2016
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