  Federated learning (FL) allows the collaborative training of AI models
without needing to share raw data. This capability makes it especially
interesting for healthcare applications where patient and data privacy is of
utmost concern. However, recent works on the inversion of deep neural networks
from model gradients raised concerns about the security of FL in preventing the
leakage of training data. In this work, we show that these attacks presented in
the literature are impractical in FL use-cases where the clients' training
involves updating the Batch Normalization (BN) statistics and provide a new
baseline attack that works for such scenarios. Furthermore, we present new ways
to measure and visualize potential data leakage in FL. Our work is a step
towards establishing reproducible methods of measuring data leakage in FL and
could help determine the optimal tradeoffs between privacy-preserving
techniques, such as differential privacy, and model accuracy based on
quantifiable metrics.
  Code is available at
https://nvidia.github.io/NVFlare/research/quantifying-data-leakage.
