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Minimax Demographic Group Fairness in Federated Learning. (arXiv:2201.08304v1 [cs.LG])
Jan. 21, 2022, 2:10 a.m. | Afroditi Papadaki, Natalia Martinez, Martin Bertran, Guillermo Sapiro, Miguel Rodrigues
cs.LG updates on arXiv.org arxiv.org
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 in federated learning scenarios where different
participating entities may only have access to a subset of the population
groups during the training phase. We formally analyze how our proposed group
fairness objective differs from existing federated learning fairness criteria
that impose similar performance across participants instead of demographic
groups. We provide an optimization …
More from arxiv.org / cs.LG updates on arXiv.org
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