Fraud detection empowered by federated learning with the Flower framework on Amazon SageMaker AI

This article discusses using federated learning with the Flower framework on Amazon SageMaker to enhance fraud detection while maintaining data privacy. Key highlights include:

• Federated learning allows financial institutions to train models collaboratively without sharing raw data
• Synthetic Data Vault (SDV) helps generate realistic datasets to improve model training
• The approach addresses privacy regulations like GDPR and CCPA
• Cross-institutional model training helps reduce overfitting and improves fraud detection accuracy
• Flower framework enables seamless integration with multiple ML tools like PyTorch and TensorFlow

The solution provides a privacy-preserving method for fraud detection that improves model performance while maintaining strict data governance across financial institutions.