  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 model fairness will decrease model performance.
Existing approaches have characterized such a trade-off by introducing
hyperparameters to quantify client's preferences for model fairness and model
performance. Nevertheless, these approaches are limited to scenarios where each
client has only a single pre-defined preference, and fail to work in practical
systems where each client generally have multiple preferences. 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) to
generate preference-wise model in real time. PraFFL can adaptively adjust the
model based on each client's preferences to meet their needs. We theoretically
prove that PraFFL can offer the optimal model tailored to an arbitrary
preference of each client, and show its linear convergence. Experimental
results show that our proposed PraFFL outperforms five fair federated learning
algorithms in terms of the model's capability of adapting to clients' different
preferences.
