  Federated learning (FL) aims to collaboratively train the global model in a
distributed manner by sharing the model parameters from local clients to a
central server, thereby potentially protecting users' private information.
Nevertheless, recent studies have illustrated that FL still suffers from
information leakage as adversaries try to recover the training data by
analyzing shared parameters from local clients. To deal with this issue,
differential privacy (DP) is adopted to add noise to the gradients of local
models before aggregation. It, however, results in the poor performance of
gradient-based interpretability methods, since some weights capturing the
salient region in feature map will be perturbed. To overcome this problem, we
propose a simple yet effective adaptive differential privacy (ADP) mechanism
that selectively adds noisy perturbations to the gradients of client models in
FL. We also theoretically analyze the impact of gradient perturbation on the
model interpretability. Finally, extensive experiments on both IID and Non-IID
data demonstrate that the proposed ADP can achieve a good trade-off between
privacy and interpretability in FL.
