A Secure and Trustworthy Network Architecture for Federated Learning Healthcare Applications

TL;DR

This paper presents a secure, flexible, and efficient federated learning network architecture tailored for healthcare applications like stroke prediction, emphasizing security, communication protocols, and practical deployment considerations.

Contribution

It introduces a Docker-based client architecture, analyzes communication protocols, and addresses security threats to enhance trustworthiness in federated learning for healthcare.

Findings

MQTT protocol is suitable for FL communication.

Docker-based client nodes improve deployment flexibility.

Security threats are identified and mitigation strategies are proposed.

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.