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Federated Learning with Privacy Preservation on Azure Edge

Edge-AI Powered Predictive

Federated Learning with Privacy Preservation on Azure Edge

  • Use Case: Hospitals, manufacturing plants, and IoT-driven industries want to train machine learning models collaboratively across distributed devices (edge nodes) without centralizing sensitive data. Federated learning enables multiple participants to train a shared model locally on their devices while ensuring data privacy and compliance (HIPAA, GDPR).

Objective

  • Enable distributed ML model training across Azure Edge devices.

    Ensure data privacy by keeping raw data local.

    Reduce latency and bandwidth costs by performing training at the edge.

    Provide secure aggregation of models with privacy-preserving techniques (differential privacy, homomorphic encryption).

Project Description

  • This project deploys a federated learning framework on Azure Edge.

    IoT/edge devices train local models on sensitive data (e.g., healthcare patient vitals, smart manufacturing sensor data).

    Only model updates (gradients/weights) are securely aggregated in the cloud without exposing raw data.

    Privacy-preserving mechanisms (differential privacy + encryption) ensure compliance.

    Centralized global model updates are redistributed to all participants via Azure cloud services.

    The system supports real-time edge AI for prediction while continuously improving the global model.
    • Key Technologies & AWS Services :
    Azure Service Purpose in Project
    Azure IoT Edge Runs ML training and inference on local devices, enabling offline and real-time processing.
    Azure Machine Learning Orchestrates federated learning experiments, manages model training/updates, and monitors performance.
    Azure Confidential Ledger Ensures tamper-proof audit trail of model updates for data integrity and provenance.
    Azure Confidential Computing Provides secure enclaves for privacy-preserving aggregation of model weights.
    Azure Functions Serverless functions to trigger model aggregation and update distribution events.
    Azure Kubernetes Service (AKS) Scales and manages federated learning orchestration across distributed edge and cloud nodes.
    Azure Event Grid Routes model updates, events, and alerts between edge devices and the cloud.
    Azure Key Vault Securely stores keys for encryption, differential privacy parameters, and certificates.
    Azure Monitor + Power BI Monitoring, visualization, and reporting of training progress, edge performance, and predictions.