README.md

⚠️ ⚠️

This repository ended up being used as the basis for the algorithms both for server and worker for the IDS solution for the HealthAI demo. It's trully a proof-of-concept. Implements kmeans (v6/Aiara), statistics (v6/Aiara), and neuralnet (TNO/Maarten).

However it does not handle multiple users, multiple tasks at the same time, authentication, etc.

Patient similarity backend

This repository contains the logic for the Federated Learning implementation through IDS for patient similarity with TNM data. Neural net training should also be possible 🚧. Different algorithm to collect statistics should be feasible to implement. This is a proof-of-concept.

The code in this repository needs the Federated Learning Data App combined with a TSG Core Container to work.

Deployment

This Data App should be deployed via the TSG Connector Helm Chart, providing the following configuration for a worker:

containers:
  # ... Federated Learning data app config here
  - type: helper
    image: ghcr.io/maastrichtu-cds/ids-healthai-patient-similarity-py:demo 
    name: patient-similarity-backend
    command: ["python3"]
    args: ["federated_learning.py"]
    tty: true
    environment:
      - name: DATA_APP_URL
        value: http://{{ template "tsg-connector.fullname" . }}-federated-learning-http:8080
    services:
      - port: 8080
        name: http
        ingress:
          path: /tf/(.*)
          rewriteTarget: /$1
          clusterIssuer: letsencrypt

Change the federated_learning.py and federated_learning_server.py for the researcher configuration.

Acknowledgments

This project was financially supported by the AiNed foundation.