A Federated Learning Platform as a Service for Advancing Stroke Management in European Clinical Centers

The rapid evolution of artificial intelligence (AI) technologies holds transformative potential for the healthcare sector. In critical situations requiring immediate decision-making, healthcare professionals can leverage machine learning (ML) algorithms to prioritize and optimize treatment options,...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2024-10
Main Authors: Diogo Reis Santos, Albert Sund Aillet, Boiano, Antonio, Milasheuski, Usevalad, Giusti, Lorenzo, Marco Di Gennaro, Kianoush, Sanaz, Barbieri, Luca, Nicoli, Monica, Carminati, Michele, Redondi, Alessandro E C, Savazzi, Stefano, Serio, Luigi
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Availability
Configuration management
Federated learning
Health care
Machine learning
Queueing
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The rapid evolution of artificial intelligence (AI) technologies holds transformative potential for the healthcare sector. In critical situations requiring immediate decision-making, healthcare professionals can leverage machine learning (ML) algorithms to prioritize and optimize treatment options, thereby reducing costs and improving patient outcomes. However, the sensitive nature of healthcare data presents significant challenges in terms of privacy and data ownership, hindering data availability and the development of robust algorithms. Federated Learning (FL) addresses these challenges by enabling collaborative training of ML models without the exchange of local data. This paper introduces a novel FL platform designed to support the configuration, monitoring, and management of FL processes. This platform operates on Platform-as-a-Service (PaaS) principles and utilizes the Message Queuing Telemetry Transport (MQTT) publish-subscribe protocol. Considering the production readiness and data sensitivity inherent in clinical environments, we emphasize the security of the proposed FL architecture, addressing potential threats and proposing mitigation strategies to enhance the platform's trustworthiness. The platform has been successfully tested in various operational environments using a publicly available dataset, highlighting its benefits and confirming its efficacy.
ISSN:2331-8422