Table of Contents

• Introduction to Federated Learning
  • FEDn: the SDK for scalable federated learning
• Quick Start
• Architecture overview
  • The clients: tier 1
  • The combiners: tier 2
  • The controller: tier 3
  • Notes on aggregating algorithms
  • Hierarchical Federated Averaging
• Distributed Deployment
  • Prerequisites
  • 1. Deploy storage and database services (MinIO, MongoDB and MongoExpress)
  • 2. Deploy the reducer
  • 3. Deploy combiners
  • 4. Attach clients to the FEDn network
• User interfaces
  • APIClient
  • Dashboard
• Tutorial: Compute Package
  • The compute package
  • Designing the compute package
  • Packaging for distribution
  • More on local data access
  • Running the client
• Frequently asked questions
  • Q: How do I remove/replace the compute package?
  • Q: Can I skip fetching the remote package and instead use a local folder when developing the compute package
  • Q: How can other aggregation algorithms can be defined?
  • Q: What is needed to include other ML frameworks in FEDn like sklearn, xgboost, etc.?
  • Q: How can I configure the round participation policy:
  • Q: How can I configure the round validity policy:
  • Q: Can I start a client listening only to training requests or only on validation requests?:
  • Q: Do you plan to support additional privacy-preserving mechanisms and frameworks?
• API reference
  • fedn (python package)

Indices and tables

• Index

• Module Index

• Search Page