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. The framework applies to both Deep Neural Networks as well as “traditional” approaches for the most common machine learning libraries.
This presentation will show how 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 as well as related architecture approach. IBM Federated Learning provides infrastructure and coordination for federated learning.
Speaker: Florin MANAILA
Executive Architect - NextGen Workloads and Distributed AI
IBM Client Engineering
Member of the IBM Academy of Technology (AoT)