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

TL;DR

This paper introduces a novel FL platform designed to support the configuration, monitoring, and management of FL processes, operating on Platform-as-a-Service (PaaS) principles and utilizes the Message Queuing Telemetry Transport (MQTT) publish-subscribe protocol.

Abstract

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.

Figures (5)

• Figure 1: TRUSTroke scheme: data from local clinical sites is harmonized to train federated models through a Parameter Server iteratively. Model results are communicated to patients and healthcare professionals. Clinical sites are continuously involved to improve the AI models and obtain clinical evidence.
• Figure 2: TRUSTroke federated learning network and infrastructure. The implementation of Client Nodes, shown on the left side, comprises two containerized applications: TRUSTroke-Jump-Host and TRUSTroke-Client. The former is responsible for communication with CERN's network and MQTT broker. The latter resides in an isolated network with data access and is responsible for training local ML models. The Broker and Parameter Server implementation is shown on the right. Based on microservices, the PS is isolated and only accessible from the MQTT broker. Experiment storage and backups are provided by the cloud infrastructure.
• Figure 3: MQTT main PS, CNs, and CC topics. Published topics are represented by blue arrows, and subscribed topics by orange arrows.
• Figure 4: Sequence diagrams for Parameter Server and Control Center interactions during FL process initialization (left). Client Nodes and Parameter Server interactions for each FL round (right).
• Figure 5: Comparative results using publicly available Stroke Dataset for local, centralized, and federated scenarios. Results are shown as the Area Under Precision-Recall Curve (AUPRC) mean and standard deviation from cross-validation.