FATE

Why we included this project

FATE exists for the awkward situation where several organizations each hold data they cannot share, but still want to train a model together. It wraps secure computation methods such as homomorphic encryption and multi-party computation around a set of federated learning algorithms, from logistic regression and tree ensembles to deep and transfer learning, so each party keeps its records local while contributing to a shared model. The project supports both single-node and cluster deployments, and the surrounding ecosystem adds scheduling, serving, and visualization, which matters when you need to move a federated model from a pilot into a real inference path. If your constraint is data residency and compliance rather than just model quality, this is a framework worth evaluating.