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.

翻译：联邦学习（FL）已成为一种有前景的隐私保护机器学习方法，尤其适用于医疗等敏感领域。在此背景下，TRUSTroke项目旨在利用FL辅助临床医生进行缺血性卒中的预测。本文概述了TRUSTroke的FL网络基础设施。所提出的架构采用客户端-服务器模型，并设置中心化参数服务器（PS）。我们为客户端节点引入了一种基于Docker的设计，为在临床环境中实现FL过程提供了灵活的解决方案。分析了不同通信协议（HTTP或MQTT）对FL网络运行的影响，最终选择MQTT因其更适用于FL场景。同时提出了一种控制平面，以支持FL过程所需的主要操作。本文最后分析了FL架构的安全性问题，探讨了潜在威胁并提出了提升可信度的缓解策略。