FedML

Why we included this project

FedML is one of the few libraries that covers the whole ML lifecycle without forcing your data into a single GPU box. Its real strength is collaborative training across silos, edge devices, smartphones, and cloud GPUs, where raw data stays put while models still get trained and refined. That makes it a good fit for privacy-sensitive workloads and on-device learning, and for cross-organization collaborations where sharing data outright is off the table. The same codebase also bundles distributed training, model serving, and a cross-cloud job scheduler, so you can go from experiment to deployment without gluing together separate tools. Developers who want a research-backed foundation with MLOps hooks, not another toy federated learning demo, will get the most out of it.