  In federated learning for medical image analysis, the safety of the learning
protocol is paramount. Such settings can often be compromised by adversaries
that target either the private data used by the federation or the integrity of
the model itself. This requires the medical imaging community to develop
mechanisms to train collaborative models that are private and robust against
adversarial data. In response to these challenges, we propose a practical
open-source framework to study the effectiveness of combining differential
privacy, model compression and adversarial training to improve the robustness
of models against adversarial samples under train- and inference-time attacks.
Using our framework, we achieve competitive model performance, a significant
reduction in model's size and an improved empirical adversarial robustness
without a severe performance degradation, critical in medical image analysis.
