32 citations · 35 across the 5 of their papers we have counts for
4 papers · 1 filter
Federated Fairness without Access to Sensitive Groups
Afroditi Papadaki, Natalia Martinez, Martin Bertran +2
Current approaches to group fairness in federated learning assume the existence of predefined and labeled sensitive groups during training. However, due to factors ranging from eme…
On the Generalization Error of Meta Learning for the Gibbs Algorithm
Yuheng Bu, Harsha Vardhan Tetali, Gholamali Aminian +2
We analyze the generalization ability of joint-training meta learning algorithms via the Gibbs algorithm. Our exact characterization of the expected meta generalization error for t…
Minimax Demographic Group Fairness in Federated Learning
Afroditi Papadaki, Natalia Martinez, Martin Bertran +2
Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models. In this work, we study minimax group fairness…
Federating for Learning Group Fair Models
Afroditi Papadaki, Natalia Martinez, Martin Bertran +2
Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models. In this work, we study minmax group fairness…