  Federated learning (FL) is a new machine learning paradigm to overcome the
challenge of data silos and has garnered significant attention. However,
through our observations, a globally effective trained model may performance
disparities in different clients. This implies that the jointly trained models
by clients may lead to unfair outcomes. On the other hand, relevant studies
indicate that the transmission of gradients or models in federated learning can
also give rise to privacy leakage issues, such as membership inference attacks.
  To address the first issue mentioned above, we propose a fairness-aware
federated learning algorithm, termed FedFair. Building upon FedFair, we
introduce privacy protection to form the FedFDP algorithm to address the second
issue mentioned above. In FedFDP, we devise a fairness-aware clipping strategy
to achieve differential privacy while adjusting fairness. Additionally, for the
extra uploaded loss values, we present an adaptive clipping approach to
maximize utility. Furthermore, we theoretically prove that our algorithm
converges and ensures differential privacy. Lastly, extensive experimental
results demonstrate that FedFair and FedFDP significantly outperform
state-of-the-art solutions in terms of model performance and fairness. Code and
data is accessible at https://anonymous.4open.science/r/FedFDP-5607.
