A Secure and Trustworthy Network Architecture for Federated Learning Healthcare Applications

2024/04/17 by Antonio Boiano, Boiano, Antonio, Marco Di Gennaro +17
Computer Science · #Artificial Intelligence (cs.AI) #Cloud Data Security Solutions #Cryptography and Data Security #Distributed #FOS: Computer and information sciences #Parallel #Privacy-Preserving Technologies in Data #and Cluster Computing (cs.DC)

paper · pdf · doi:10.48550/arxiv.2404.11698

openalex publication_date 2024/04/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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