Federated Learning (FL) is a machine learning approach that allows for the training of a model across multiple decentralized devices or institutions, without the need to centralize the data on a single server. It has been used across several industries, from mobile device keyboards to autonomous vehicles to oil rigs. It is particularly useful in the healthcare industry, where sensitive patient data is involved and strict regulations need to be followed to protect the privacy of individuals. In this blog post, we will discuss some practical steps to implementing a federated learning project with healthcare data. First, it is important to understand the requirements and constraints of your project.