This article explains how to implement federated learning on Amazon SageMaker using the Flower framework to train ML models on decentralized data across AWS accounts while maintaining data privacy.

• Federated learning enables parallel training sessions across geographic boundaries with local models aggregated into a global model
• Differs from distributed training: FL keeps data decentralized across accounts/regions; distributed training centralizes data in one region
• Flower framework chosen for extensibility, customization, and lightweight design supporting multiple ML frameworks
• Architecture uses VPC peering, cross-account IAM roles, and gRPC for secure communication between server and clients
• Client account runs SageMaker training jobs with Flower client code; server account orchestrates federation and model aggregation
• Implementation uses scikit-learn logistic regression model trained on Medicare fraud detection dataset
• Raw training data never leaves client accounts; only derived model weights transmitted across peered connections
• Demonstrates complete setup including networking, IAM configuration, client/server code, and model evaluation

This solution enables organizations to build ML models on sensitive data distributed across accounts without centralizing or sharing raw data, addressing privacy and regulatory compliance requirements.