Federated Learning Meets Fairness and Differential Privacy

Autor:	Padala, Manisha, Damle, Sankarshan, Gujar, Sujit
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Cryptography and Security Computer Science - Computers and Society
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2108.09932 Zobrazit plný text záznamu View this record from Arxiv