  Federated learning (FL) as one of the novel branches of distributed machine
learning (ML), develops global models through a private procedure without
direct access to local datasets. However, access to model updates (e.g.
gradient updates in deep neural networks) transferred between clients and
servers can reveal sensitive information to adversaries. Differential privacy
(DP) offers a framework that gives a privacy guarantee by adding certain
amounts of noise to parameters. This approach, although being effective in
terms of privacy, adversely affects model performance due to noise involvement.
Hence, it is always needed to find a balance between noise injection and the
sacrificed accuracy. To address this challenge, we propose adaptive noise
addition in FL which decides the value of injected noise based on features'
relative importance. Here, we first propose two effective methods for
prioritizing features in deep neural network models and then perturb models'
weights based on this information. Specifically, we try to figure out whether
the idea of adding more noise to less important parameters and less noise to
more important parameters can effectively save the model accuracy while
preserving privacy. Our experiments confirm this statement under some
conditions. The amount of noise injected, the proportion of parameters
involved, and the number of global iterations can significantly change the
output. While a careful choice of parameters by considering the properties of
datasets can improve privacy without intense loss of accuracy, a bad choice can
make the model performance worse.
