  Federated Learning (FL) is an approach to conduct machine learning without
centralizing training data in a single place, for reasons of privacy,
confidentiality or data volume. However, solving federated machine learning
problems raises issues above and beyond those of centralized machine learning.
These issues include setting up communication infrastructure between parties,
coordinating the learning process, integrating party results, understanding the
characteristics of the training data sets of different participating parties,
handling data heterogeneity, and operating with the absence of a verification
data set.
  IBM Federated Learning provides infrastructure and coordination for federated
learning. Data scientists can design and run federated learning jobs based on
existing, centralized machine learning models and can provide high-level
instructions on how to run the federation. The framework applies to both Deep
Neural Networks as well as ``traditional'' approaches for the most common
machine learning libraries. {\proj} enables data scientists to expand their
scope from centralized to federated machine learning, minimizing the learning
curve at the outset while also providing the flexibility to deploy to different
compute environments and design custom fusion algorithms.
