• Corpus ID: 240288729

Improving Fairness via Federated Learning

@article{Zeng2021ImprovingFV,
  title={Improving Fairness via Federated Learning},
  author={Yuchen Zeng and Hongxu Chen and Kangwook Lee},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.15545}
}
Recently, lots of algorithms have been proposed for learning a fair classifier from centralized data. However, how to privately train a fair classifier on decentralized data has not been fully studied yet. In this work, we first propose a new theoretical framework, with which we analyze the value of federated learning in improving fairness. Our analysis reveals that federated learning can strictly boost model fairness compared with all non-federated algorithms. We then theoretically and… 
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