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Federated Learning for Healthcare on Azure: Training MRI Segmentation Models Across Distributed Hospitals without Sharing Raw Data

Federated Learning for Healthcare

Federated Learning for Healthcare on Azure

  • Use Case:

    Medical imaging datasets (e.g., MRIs for tumor segmentation) are sensitive and protected under strict privacy regulations (HIPAA, GDPR). Hospitals often cannot share raw patient data across institutions, limiting centralized AI model training.

    Federated Learning (FL) allows hospitals to collaboratively train AI models by sharing only model updates/gradients, not raw data.

    Using Azure’s cloud-edge ecosystem, hospitals can train MRI segmentation models locally and aggregate them centrally, enabling privacy-preserving, distributed AI in healthcare.

Objective

  • To design a federated learning framework on Azure for MRI brain tumor segmentation.

    To allow multiple hospitals to train local segmentation models without sharing sensitive MRI data.

    To aggregate local model updates in the cloud for a global model with improved generalization.

    To demonstrate compliance with data privacy regulations while improving AI accuracy in healthcare.

Project Description

  • Data Distribution Setup :

    Each participating hospital stores its own MRI dataset locally (e.g., using Azure Stack Edge or on-prem systems).

    Data never leaves the hospital’s secure boundary.

Local Model Training (Hospital Level)

  • Each hospital trains a U-Net or DeepLabV3+ segmentation model locally on MRI scans.

    Models are trained using Azure ML on local compute or IoT Edge devices.

    After training, only model weights/gradients are shared to the global aggregator.

Global Aggregation (Cloud Level)

  • Use Azure Machine Learning Service as the central federated server.

    Perform Federated Averaging (FedAvg) to combine weights from distributed hospitals.

    Update the global model and redistribute it to all hospitals.

Evaluation

  • Measure segmentation performance (Dice coefficient, IoU, Precision, Recall).
  • Compare federated model performance vs. individual local models.

Deployment

  • Deploy the final global MRI segmentation model on Azure Kubernetes Service (AKS) or Azure Stack Edge.
  • Enable hospitals to use the model for real-time MRI segmentation assistance.
  • Azure Services Used :
    Azure Service Purpose
    Azure Machine Learning Service Central federated server for global model aggregation and coordination
    Azure IoT Edge / Azure Stack Edge Local training of models at hospital sites while keeping data secure
    Azure Blob Storage / Data Lake Storage of encrypted model weights/updates and metadata (not raw patient data)
    Azure Kubernetes Service (AKS) Deployment of the final global segmentation model as an API or service
    Azure Confidential Computing Privacy-preserving computation for secure model aggregation
    Azure Container Registry (ACR) Storage of Dockerized FL clients and server components
    Azure Monitor & Application Insights Monitoring federated training jobs and global model performance