Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker

This article discusses federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker. Federated learning is a decentralized machine learning approach that allows training on distributed datasets without sharing raw data, addressing data privacy concerns.

Specifically, the article covers:

• The need for federated learning in healthcare to enable collaborative model training while ensuring data privacy and regulatory compliance
• An overview of the FedML framework and its components like FedML Octopus and FedML MLOps
• The solution architecture using Amazon EKS and Amazon SageMaker for federated learning deployment
• Prerequisites and steps to deploy the solution using Terraform
• Creating and uploading FedML packages to the MLOps platform
• Triggering federated training and tracking experiments using Amazon SageMaker Experiments
• Code snippets for integrating SageMaker experiment tracking with the federated learning framework
• Cleaning up the deployed resources