PraFFL: A Preference-Aware Scheme in Fair Federated Learning
Rongguang Ye Ming Tang
Fairness in federated learning has emerged as a critical concern, aiming to
develop an unbiased model for any special group (e.g., male or female) of
sensitive features. However, there is a trade-off between model performance and
fairness, i.e., improving fairness will decrease model performance. Existing
approaches have characterized such a trade-off by introducing hyperparameters
to quantify client's preferences for fairness and model performance.
Nevertheless, these methods are limited to scenarios where each client has only
a single pre-defined preference. In practical systems, each client may
simultaneously have multiple preferences for the model performance and
fairness. The key challenge is to design a method that allows the model to
adapt to diverse preferences of each client in real time. To this end, we
propose a Preference-aware scheme in Fair Federated Learning paradigm (called
PraFFL). PraFFL can adaptively adjust the model based on each client's
preferences to meet their needs. We theoretically prove that PraFFL can provide
the optimal model for client's arbitrary preferences. Experimental results show
that our proposed PraFFL outperforms five existing fair federated learning
algorithms in terms of the model's capability in adapting to clients' different
preferences.