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Developing a Federated Learning Framework for Edge IoT Devices using Google Cloud IoT Core and TensorFlow Lite

IoT Sensor Data

Edge IoT Devices using Google Cloud IoT Core and TensorFlow Lite

  • Use Case : IoT devices generate massive amounts of sensitive data (e.g., healthcare vitals, industrial sensors, smart home devices). Sending all raw data to the cloud can violate privacy, incur bandwidth costs, and increase latency. Federated Learning (FL) enables training ML models locally on edge devices without sharing raw data, only exchanging model updates.

Objective

  • Implement a privacy-preserving federated learning framework for distributed edge IoT devices.

    Enable collaborative ML model training without centralizing sensitive data.

    Reduce network bandwidth usage and latency while maintaining model accuracy.

    Leverage Google Cloud IoT Core for device management and TensorFlow Lite for edge ML inference and updates.

Project Description

  • This project builds a federated learning pipeline for IoT devices:

    Device Registration & Management: Register edge devices securely using Cloud IoT Core.

    Local Model Training: Deploy initial ML models to IoT devices using TensorFlow Lite; train locally on device-specific data.

    Model Update Aggregation: Edge devices send model weight updates (not raw data) to a central server.

    Global Model Update: Aggregate updates using a cloud-based orchestrator (Vertex AI or Cloud ML Engine) and update global model.

    Model Redistribution: Push updated global model back to devices for the next training iteration.

    Monitoring & Evaluation: Track model convergence, training accuracy, and device participation.

Key Technologies & Google Cloud Platform Services

  • GCP Service Purpose
    Cloud IoT Core Securely registers, manages, and connects edge devices; streams device metrics.
    TensorFlow Lite Runs lightweight ML models on edge devices; supports on-device training and inference.
    Pub/Sub Streams model updates and orchestrates communication between edge devices and cloud aggregator.
    Vertex AI / Cloud ML Aggregates model updates from devices; maintains and updates the global federated model.
    Cloud Storage Stores global model checkpoints, training logs, and intermediate datasets.
    BigQuery Analyzes aggregated training metadata, model performance, and device participation.
    Cloud Functions Automates model update triggers, notifications, and orchestration workflows.
    Cloud Monitoring / Logging Monitors device connectivity, training progress, and system performance.
    Cloud Key Management Service (KMS) Encrypts sensitive model updates and communication for secure federated learning.
    Looker / Data Studio Visualizes model accuracy trends, device participation metrics, and federated learning performance.