Emergent Mind
Federated Learning Meets Fairness and Differential Privacy
(2108.09932)
Published Aug 23, 2021
in
cs.LG
,
cs.AI
,
cs.CR
,
and
cs.CY
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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