Related papers: Federated Learning Meets Fairness and Differential Privacy

Federated Learning Meets Fairness and Differential Privacy

URL: http://arxiv.org/abs/2108.09932v1
Date: Mon, 23 Aug 2021 04:59:16 GMT
Title: Federated Learning Meets Fairness and Differential Privacy
Authors: Manisha Padala, Sankarshan Damle and Sujit Gujar
Abstract summary: 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.
Score: 12.033944769247961
License: http://creativecommons.org/licenses/by/4.0/
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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