Tutorials

FedERA allows you to do federated learning in real time on various supported edge devices, including Intel CPUs, Nvidia GPUs, Nvidia Jetson, Raspberry Pi, Intel NUC. FedERA provides modular tools and standard algorithms to simplify federated learning implementation in real time using gRPC framework.

Running Server and Client

Step-by-step guide on running server and clients on same and different devices.

How to Customize Federated Learning Algorithm?

Step-by-step guide on how to customize federated learning algorithm.

How to add Custom Dataset?

Step-by-step guide on how to add a custom dataset.

How to add Custom Model?

Step-by-step guide on how to add a custom model.

How to add use different Data Distribution?

Step-by-step guide on how to use different data dstributions while training.

How to get Carbon Footprint?

Step-by-step guide on how to get carbon footprint of the training process.

How to use Encryption?

Step-by-step guide on how to use encryption in the training process.

• Running the Server and Clients
  • Starting the Server
  • Starting the Clients
• Federated Learning Algorithms
  • Adding a new algorithm to FedERA
• Datasets
  • Adding support for new datasets
• Models
  • Adding support for a new model
• Data Distribution
  • Visualizing the non-IID data distribution for MNIST dataset
• Carbon Emissions
• Encryption
  • TLS Basics
  • Generate Certificates
  • TLS Server Identification and Encryption
  • Acknowledgments