Federated Learning Meets Fairness and Differential Privacy
This work addresses ethical issues like bias and privacy for federated learning applications, but it is incremental as it combines existing measures.
The paper tackled the problem of ethical concerns in deep learning by developing a federated learning model that simultaneously incorporates fairness metrics and differential privacy, and experiments on three datasets demonstrated the empirical interplay between accuracy, fairness, and privacy.
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.