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OctaiPipe 2.5.0 documentation
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OctaiPipe 2.5.0 documentation

Contents:

  • Getting Started Guide
  • Installation and Setup
    • Installing OctaiPipe from the Azure Marketplace
    • Upgrading a Deployed Version of OctaiPipe
    • Register a Device
    • Setting up a Jupyter Notebook Server
  • Portal Interface
  • Python Interface
  • Tutorials
    • Tutorial - From training to inference - Classification
    • Tutorial - From training to inference - Remaining Useful Life
    • Tutorial - Using MLOps policies
    • Tutorial - Federated Learning Anomaly Detection
    • Tutorial - Running FL XGBoost with OctaiPipe
    • Tutorial - Adversarial Fortification for FL XGBoost
    • Tutorial - Federated Cluster Detection with k-FED
    • Tutorial - Deploying Image based Inference to an Android Handset
    • Guide - Custom Pipeline Steps
    • Guide - Remaining Useful Life Estimation
  • OctaiPipe Components
    • OctaiPipe Images
    • OctaiPipe Modules
    • Docker Resource Constraints
    • OctaiPipe Models
    • Data Loading and Writing Utilities
    • Model and Preprocessor Object Management
  • OctaiPipe Steps
    • Preprocessing Step
    • Feature Engineering Step
    • Model Training Step
    • Model Evaluation Step
    • Model Inference Step
    • WASM Model Inference Step
    • Data Drift Monitoring Step
    • Feature Selection Step
    • Data Writing Step
    • Custom Pipeline Steps
      • Custom pipeline step
    • Android Inference Step
  • Federated EDA
  • Edge Labelling
    • Active Learning
  • Federated Learning
    • Supervised Federated Learning
      • Federated PyTorch
      • Federated Sklearn
      • Federated XGBoost
    • Unsupervised Federated Learning
      • k-FED Clustering
        • Evaluating k-FED
    • FL Train Step
    • Aggregation Strategies
    • Intermittent Devices
    • Adversarial Fortification
      • Adversarial Fortification for XGBoost
    • Differential Privacy (DP) Implementation with OctaiPipe
  • Cloud Deployment
    • Deploying to Cloud
    • Step Deployment
    • Generic Cloud Deployment
    • Inference API Deployment
    • InfluxDB Deployment
    • Grafana Deployment
  • Infrastructure Cost Management
  • Frequently Asked Questions
  • Release Notes
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Supervised Federated Learning#

Supervised Federated Learning

  • Federated PyTorch
  • Federated Sklearn
  • Federated XGBoost
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