  Federated Learning (FL) has emerged as a promising approach for
privacy-preserving machine learning, particularly in sensitive domains such as
healthcare. In this context, the TRUSTroke project aims to leverage FL to
assist clinicians in ischemic stroke prediction. This paper provides an
overview of the TRUSTroke FL network infrastructure. The proposed architecture
adopts a client-server model with a central Parameter Server (PS). We introduce
a Docker-based design for the client nodes, offering a flexible solution for
implementing FL processes in clinical settings. The impact of different
communication protocols (HTTP or MQTT) on FL network operation is analyzed,
with MQTT selected for its suitability in FL scenarios. A control plane to
support the main operations required by FL processes is also proposed. The
paper concludes with an analysis of security aspects of the FL architecture,
addressing potential threats and proposing mitigation strategies to increase
the trustworthiness level.
