Machine learning (ML) is being used by organizations to improve business decision-making by utilizing large datasets. However, sharing raw sensitive information poses security risks. Federated learning (FL) is a decentralized ML training technique that maintains data privacy while collaborating on model training. FL addresses data privacy concerns by training models within isolated client locations and sharing only model parameters with a centralized server rather than raw data. While FL enhances data privacy, insecure networks and inference attacks still pose risks.
In healthcare, where data privacy is crucial due to regulations like HIPAA, implementing FL solutions requires robust encryption and access controls. The use case discussed involves using FL to collaborate on predicting heart disease across different organizations, leveraging the Heart Disease dataset from the University of California Irvine.
The FedML framework supports various FL paradigms and provides tools for developing FL solutions. FedML Octopus is used for cross-silo FL in scenarios with diverse infrastructures, while FedML MLOps facilitates the development and deployment of FL solutions. By deploying FedML into Amazon EKS clusters integrated with SageMaker, organizations can scale training and deployment of shared models while maintaining data privacy.
Experiment tracking is implemented using SageMaker Experiments to monitor model performance, parameters, and metrics. To trigger federated training, developers must create and upload client and server packages to the FedML MLOps platform. The training process involves creating projects, starting training runs, updating hyperparameters, and tracking progress.
Cleanup is essential to optimize resource utilization and reduce costs after completing the solution. By using Amazon EKS and FedML, organizations can securely collaborate, utilize distributed data effectively, and enhance ML models without compromising data privacy. The authors of this post, including experts from AWS and FedML, share their insights and experiences in implementing FL solutions and ML frameworks.
Article Source
https://aws.amazon.com/blogs/machine-learning/federated-learning-on-aws-using-fedml-amazon-eks-and-amazon-sagemaker/
