Federated Learning Meets Fairness and Differential Privacy

Manisha Padala; Sankarshan Damle; Sujit Gujar

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arXiv · 2108.09932

Federated Learning Meets Fairness and Differential Privacy

Manisha Padala·Sankarshan Damle·Sujit Gujar

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Abstract

Deep learning's unprecedented success raises several ethical concerns ranging from biased predictions to data privacy. Researchers tackle these issues by introducing fairness metrics, or federated learning, or differential privacy. A first, this work presents an ethical federated learning model, incorporating all three measures simultaneously. Experiments on the Adult, Bank and Dutch datasets highlight the resulting ``empirical interplay" between accuracy, fairness, and privacy.

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Manisha Padala, Sankarshan Damle, Sujit Gujar. 2021-08-23. Federated Learning Meets Fairness and Differential Privacy. https://arxiv.org/abs/2108.09932

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