FedML
Basic Information
FedML is a unified and scalable machine learning library aimed at enabling large-scale distributed training, model serving, and federated learning. The repository provides a consolidated set of tools and components for launching and managing ML workloads across multiple devices and networked nodes. It targets researchers, ML engineers, and organizations that need to train models at scale or deploy models in distributed and privacy-sensitive environments. The project description highlights capabilities for distributed training pipelines, model serving, and federated learning workflows, and mentions a Launch component to help orchestrate experiments and deployments. The repo serves as an integrated platform to reduce friction when moving from research prototypes to production-scale distributed ML systems.