LAUSR

Federated learning (FL) that can train using machine learning methods without moving data have attracted interest owing to the focus on data privacy. Several FL platforms and frameworks are being developed with various open datasets. However, FL has not yet been fully utilized in real-world projects; instead, centralized ML models are still being used for AI. Since FL is composed of numerous clients and executed, it is necessary to manage the lifecycle such as model deployment and status management to multiple clients in order to operate FL. This study proposes FLScalize to enable AI researchers to apply their own custom data and models to FL environments and to deploy and manage the FL lifecycle. Researchers who develop these models should be able to easily and conveniently apply custom data and models developed in a centralized environment to FL environments, deploy and train multiple clients, and manage the lifecycle of the entire FL process. FLScalize can be used to simulate system heterogeneity and data heterogeneity, both of which are FL issues that occur in real FL environments. Furthermore, FLScalize provides a manager component that continuously manages the FL client and server required for real-world FL tasks and realizes an FL lifecycle management implementation that enables continuous integration, deployment, and training.