IBM Federated Learning: an Enterprise Framework White Paper V0.1
This is an incremental enterprise framework for organizations needing privacy-preserving machine learning without centralizing data.
The paper introduces IBM Federated Learning, a framework that tackles the challenges of federated machine learning, such as communication setup and data heterogeneity, by providing infrastructure and coordination tools, enabling data scientists to adapt centralized models to federated settings with minimal learning curve.
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.