Many organizations are using machine learning (ML) to enhance their business decision-making processes through automation and by leveraging large distributed datasets. However, the sharing of raw, sensitive data in different locations brings about significant security and privacy risks. To combat these issues, federated learning (FL), a decentralized and collaborative ML training technique, is used. Traditional ML training differs from FL training, which happens within an isolated client location using an independent secure session. The client shares only its output model parameters and not the actual data used to train the model with a centralized server. This approach alleviates many data privacy issues while also enabling effective collaboration on model training.
Despite FL’s potential, it’s not entirely foolproof. Insecure networks that lack access control and encryption can still leak sensitive data to attackers. Locally trained info can also reveal private data if reconstructed via an inference attack. To minimize these risks, the FL model uses personalized training algorithms, effective masking, and parameterization before sharing information with the training coordinator.
In a joint venture with FedML, this post discusses using Amazon Elastic Kubernetes Service (Amazon EKS), and Amazon SageMaker in an FL approach primarily intended to improve patient outcomes while also taking into account data privacy and security concerns. The use case in question revolves around heart disease data which spans across different organizations.
FedML was chosen for this use case because it’s open-source and supports several FL paradigms. It contains a library, platform, and application ecosystem for FL that enables the deployment of FL solutions. FedML addresses the issues of data privacy, communication, and model aggregation in FL, by offering a user-friendly interface and customizable components.
System hierarchy and heterogeneity are significant challenges in real-life FL use cases. Different data silos could have different infrastructure with CPU and GPUs. In such cases, tools like FedML Octopus can be used. FedML Octopus is an industrial-grade cross-silo FL platform ideal for cross-organization and cross-account training.
The Amazon EKS Blueprints for Terraform is used for deploying the required infrastructure. It helps compose complete EKS clusters that are fully bootstrapped with the operational software that is needed to deploy and operate workloads.
In conclusion, by using Amazon EKS as the infrastructure and FedML as the framework for FL, we can provide a scalable and managed environment for training and deploying shared models while respecting data privacy. The decentralized nature of FL allows organizations to collaborate securely, unlock the potential of distributed data, and improve ML models without compromising data privacy.
