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  • Home
  • User Documentation
  • About
  • More
    • Funding
    • News
    • Contributors
    • Users
    • Roadmap
    • Contact Us
  • Getting Started
    • What's Fed-BioMed
    • Fedbiomed Architecture
    • Fedbiomed Workflow
    • Installation
    • Basic Example
    • Configuration
  • Tutorials
    • PyTorch
      • PyTorch MNIST Basic Example
      • How to Create Your Custom PyTorch Training Plan
      • PyTorch Used Cars Dataset Example
      • PyTorch aggregation methods in Fed-BioMed
      • Transfer-learning in Fed-BioMed tutorial
    • MONAI
      • Federated 2d image classification with MONAI
      • Federated 2d XRay registration with MONAI
    • Scikit-Learn
      • MNIST classification with Scikit-Learn Classifier (Perceptron)
      • Fed-BioMed to train a federated SGD regressor model
      • Implementing other Scikit Learn models for Federated Learning
    • Optimizers
      • Advanced optimizers in Fed-BioMed
    • FLamby
      • General Concepts
      • FLamby integration in Fed-BioMed
    • Advanced
      • In Depth Experiment Configuration
      • PyTorch model training using a GPU
      • Breakpoints
    • Security
      • Using Differential Privacy with OPACUS on Fed-BioMed
      • Local and Central DP with Fed-BioMed: MONAI 2d image registration
      • Training Process with Training Plan Management
      • Training with Secure Aggregation
      • End-to-end Privacy Preserving Training and Inference on Medical Data
    • Biomedical data
      • Brain Segmentation
  • User Guide
    • Glossary
    • Deployment
      • Introduction
      • VPN Deployment
      • Network matrix
      • Security model
    • Node
      • Configuring Nodes
      • Deploying Datasets
      • Training Plan Management
      • Using GPU
      • Node GUI
    • Researcher
      • Training Plan
      • Training Data
      • Experiment
      • Aggregation
      • Listing Datasets and Selecting Nodes
      • Model Validation on the Node Side
      • Tensorboard
    • Optimization
    • Secure Aggregation
      • Introduction
      • Configuration
      • Managing Secure Aggregation in Researcher
  • Developer
    • API Reference
      • Common
        • Certificate Manager
        • CLI
        • Config
        • Constants
        • Data
        • DB
        • Exceptions
        • IPython
        • Json
        • Logger
        • Message
        • Metrics
        • Model
        • MPC controller
        • Optimizers
        • Privacy
        • Secagg
        • Secagg Manager
        • Serializer
        • Singleton
        • Synchro
        • TasksQueue
        • TrainingPlans
        • TrainingArgs
        • Utils
        • Validator
      • Node
        • CLI
        • CLI Utils
        • Config
        • DatasetManager
        • HistoryMonitor
        • Node
        • NodeStateManager
        • Requests
        • Round
        • Secagg
        • Secagg Manager
        • TrainingPlanSecurityManager
      • Researcher
        • Aggregators
        • CLI
        • Config
        • Datasets
        • Federated Workflows
        • Filetools
        • Jobs
        • Monitor
        • NodeStateAgent
        • Requests
        • Secagg
        • Strategies
      • Transport
        • Client
        • Controller
        • NodeAgent
        • Server
    • Usage and Tools
    • Continuous Integration
    • Definition of Done
    • Development Environment
    • Testing in Fed-BioMed
    • RPC Protocol and Messages
  • FAQ & Troubleshooting

Researcher component user guide

  • Training Plan
  • Training Data
  • Experiment
  • Aggregation
  • Client selection strategies
  • Listing datasets and selecting nodes
  • Training plan validation
  • Tensorboard
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