Privacy-Preserving Machine Learning

6

Federated Learning and Implementing FL Using Open Source Frameworks

In this chapter, you will learn about Federated Learning (FL) and how to implement it using open source frameworks. We will cover why it is needed and how to preserve data privacy. We will also look at the definition of FL, as well as its characteristics and the steps involved in it.

We will cover the following main topics:

• FL
• FL algorithms
• The steps involved in implementing FL
• Open source frameworks for implementing FL
• An end-to-end use case of implementing fraud detection using FL
• FL with differential privacy

By exploring these topics, you will gain a comprehensive understanding of the need for FL and the open source frameworks for implementing FL.

Federated learning