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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